In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 September1 October 2021; pp. In Proceedings of the 2019 19th International Conference on Advanced Robotics (ICAR), Horizonte, Brazil, 26 December 2019; pp. Davison, A.J. [, Gao, X.; Wang, R.; Demmel, N.; Cremers, D. LDSO: Direct Sparse Odometry with Loop Closure. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. Robotics. On the other hand, it provides a more detailed and accurate reconstruction, which may be a key factor in a SLAM project. [. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, [. 15. ; Xie, L. VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM. Cadena, C.; Carlone, L.; Carrillo, H.; Latif, Y.; Scaramuzza, D.; Neira, J.; Reid, I.; Leonard, J.J. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age. ; writingreview and editing, M.M., Y.M., G.C. MonoSLAM requires a known target for the initialization step, which is not always accessible. A high speed iterative closest point tracker on an FPGA platform. ; Emani, M.; Mawer, J.; Kotselidis, C.; Nisbet, A.; Lujan, M.; et al. Doherty, K.; Fourie, D.; Leonard, J. Multimodal Semantic SLAM with Probabilistic Data Association. The aim is to provide a snapshot of some of the 298304. methods, instructions or products referred to in the content. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2328 June 2013; pp. 701710. The descriptor data and keypoint location compose the feature, i.e., the data used by the algorithm to process the tracking and mapping. Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. 225234. Robotics. Save time finding and organizing research with Mendeley. Considering a general point of view, the visual-only-based SLAM algorithms may be considered a well-explored field, since most of the algorithms were made available by the authors, which also had consequences for the embedded SLAM implementations found in the literature. 46794685. Mur-Artal, R.; Tards, J.D. A New Hyperloop Transportation System: Design and Practical Integration. Whelan, T.; Kaess, M.; Johannsson, H.; Fallon, M.; Leonard, J.J.; McDonald, J. Real-time large-scale dense RGB-D SLAM with volumetric fusion. A Comprehensive Survey of Visual SLAM Algorithms. Zhang, S.; Zheng, L.; Tao, W. Survey and Evaluation of RGB-D SLAM. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments. Yousif, K.; Bab-Hadiashar, A.; Hoseinnezhad, R. An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. Efficient implementation of EKF-SLAM on a multi-core embedded system. [doi] Abstract. [. In Proceedings of the 2010 IEEE/SICE International Symposium on System Integration, Sendai, Japan, 2122 December 2010; pp. Most of them have high . A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation. In Proceedings of the 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy, 2527 February 2019; pp. See further details. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2024 May 2019; pp. 40724077. Burri, M.; Nikolic, J.; Gohl, P.; Schneider, T.; Rehder, J.; Omari, S.; Achtelik, M.; Siegwart, R. The EuRoC micro aerial vehicle datasets. ; Gattass, M.; Meggiolaro, M.A. Learn more about DOAJs privacy policy. A high-performance system-on-chip architecture for direct tracking for SLAM. Schubert, D.; Goll, T.; Demmel, N.; Usenko, V.; Stckler, J.; Cremers, D. The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. Researchers can consider each criterion according to their application, and obtain an initial analysis from the presented paper. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. 326329. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. and F.C. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. According to Stachniss [, Visual-based SLAM algorithms can be considered especially attractive, due to their sensor configuration simplicity, miniaturized size, and low cost. [, Boikos, K.; Bouganis, C.S. KinectFusion is an impressive algorithm that was introduced in 2011 to simultaneously track the movement of a depth camera in the 3D space and densely reconstruct the environment as a Truncated Signed Distance Formula (TSDF) volume, in real-time. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Comput. * Global Optimization. Mendeley helps you to discover research relevant for your work. ; Yuan, S.; Cao, M.; Nguyen, T.H. [, Bodin, B.; Nardi, L.; Zia, M.Z. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM . Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. For Available online: Klein, G.; Murray, D. Parallel Tracking and Mapping on a camera phone. Smart Cleaner: A New Autonomous Indoor Disinfection Robot for Combating the COVID-19 Pandemic. Map density: in general, dense reconstruction requires more computational resources than a sparse one, having an impact on memory usage and computational cost. [, Tateno, K.; Tombari, F.; Laina, I.; Navab, N. CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Deng, X.; Zhang, Z.; Sintov, A.; Huang, J.; Bretl, T. Feature-constrained Active Visual SLAM for Mobile Robot Navigation. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. At last, it only reconstructs a map of landmarks, which may be a drawback regarding the applications that require a more accurate reconstruction. 6673. Huang, G. Visual-Inertial Navigation: A Concise Review. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. 38493856. A Comprehensive Survey of Visual SLAM Algorithms. Feature In Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2630 May 2015; pp. This criterion depends on each algorithms hardware constraints and specificity, since there must be a trade-off between algorithm architecture in terms of energy consumption, memory, and processing usage. https://www.mdpi.com/openaccess. The literature presents many different visual-SLAM algorithms that make researchers choices difficult, without criteria, when it comes to evaluating their benefits and drawbacks. Some popular SLAM methods including ORB-SLAM [7,8,9], LSD-SLAM , and DSO-SLAM have been developed in these years. We release the code as an open-source package, using the Robotic Operating System (ROS) and the Point Cloud Library (PCL). [, Seiskari, O.; Rantalankila, P.; Kannala, J.; Ylilammi, J.; Rahtu, E.; Solin, A. HybVIO: Pushing the Limits of Real-Time Visual-Inertial Odometry. An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems. 30493054. 24362440. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2126 July 2017; pp. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS. However, Visual-SLAM is known to be resource-intensive in memory and processing time. Available online: Xu, Z.; Yu, J.; Yu, C.; Shen, H.; Wang, Y.; Yang, H. CNN-based Feature-point Extraction for Real-time Visual SLAM on Embedded FPGA. ; Riley, G.D.; et al. Macario Barros, A., Michel, M., Moline, Y., Corre, G., & Carrel, F. (2022, February 1). Macario Barros, A.; Michel, M.; Moline, Y.; Corre, G.; Carrel, F. A Comprehensive Survey of Visual SLAM Algorithms. Enter the email address you signed up with and we'll email you a reset link. Therefore, numerous visual-based techniques are proposed in the literature, which make the choice of the most suitable one according to ones project constraints difficult. https://doi.org/10.3390/robotics11010024, Macario Barros A, Michel M, Moline Y, Corre G, Carrel F. A Comprehensive Survey of Visual SLAM Algorithms. Loo, S.Y. ; Wu, K.; Hesch, J.A. The term visual SLAM defines the problem of build a map of an environment and perform location, simultaneously. Xiao, L.; Wang, J.; Qiu, X.; Rong, Z.; Zou, X. Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. 224229. ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction. Previous Article in Journal. Chuanfu Shen, Shiqi Yu, Jilong Wang, George Q. Huang, Liang Wang. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Available online: Visual-Inertial Dataset. Forster, C.; Zhang, Z.; Gassner, M.; Werlberger, M.; Scaramuzza, D. SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems. DOAJ 2022 default by all rights reserved unless otherwise specified. Visual-Inertial Monocular SLAM With Map Reuse. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May7 June 2014; pp. For the visual-only algorithms, we divide them into feature-based, hybrid, and direct methods. [. Please note that many of the page functionalities won't work as expected without javascript enabled. Vision SLAM or V-SLAM refers to those SLAM systems which use cameras as the main input sensors to receive visual information of unknown objects and environments. The Feature Paper can be either an original research article, a substantial novel research study that often involves Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. Furthermore, we propose six criteria that ease the SLAM algorithms analysis and consider both the software and hardware levels. Engel, J.; Schps, T.; Cremers, D. LSD-SLAM: Large-Scale Direct Monocular SLAM. In Proceedings of the 2014 International Conference on Field-Programmable Technology (FPT), Shanghai, China, 1012 December 2014; pp. 49584965. Visual-based SLAM techniques play a significant role in this field,. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely The visual-only SLAM system may use a monocular or stereo camera. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Nikolic, J.; Rehder, J.; Burri, M.; Gohl, P.; Leutenegger, S.; Furgale, P.T. In Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China, 2730 July 2019; pp. 3Focusing on the readers initiating their studies on the SLAM algorithms, we propose six main criteria to be observed in the different techniques and implementations to be considered according to ones application. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 68 November 2020; pp. 16801687. LSD-SLAM: Large-Scale Direct Monocular SLAM. ; Kohi, P.; Shotton, J.; Hodges, S.; Fitzgibbon, A. KinectFusion: Real-time dense surface mapping and tracking. Zuiga-Nol, D.; Moreno, F.A. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. Motion removal for reliable RGB-D SLAM in dynamic environments. [. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 48 January 2022; pp. All authors have read and agreed to the published version of the manuscript. The inertial data are provided by the use of an inertial measurement unit (IMU), which consists of a combination of gyroscope, accelerometer, and, additionally, magnetometer devices. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2529 October 2020; pp. [. Another main benchmark dataset is the ICL-NUIM [, A dataset commonly used to evaluate monocular systems is the TUM MonoVO [. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. In. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September3 October 2015; pp. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2529 October 2020; pp. 25022509. You can download the paper by clicking the button above. 14491456. Robotics. Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, CC0 1.0 Universal (CC0) Public Domain Dedication. 23202327. Laboratoire Capteurs et Architectures lectroniques (LCAE), Laboratoire dIntgration des Systmes et des Technologies (LIST), Commissariat lnergie Atomique et aux nergies Alternatives (CEA), 91400 Saclay, France. [, Soares, J.C.V. Robotics 2022, 11, 24. [. Zhao, C.; Sun, Q.; Zhang, C.; Tang, Y.; Qian, F. Monocular depth estimation based on deep learning: An overview. 127136. Mur-Artal, R.; Tards, J.D. Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Moreover, we present different methods for keeping the camera fixed with respect to the moving volume, fusing also IMU data and the camera heading/velocity estimation. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. In this work, we further develop the Moving Volume KinectFusion method (as rxKinFu) to fit better to robotic and perception applications, especially for locomotion and manipulation tasks. The front-end of filtering-based approaches for VI-SLAM relies on feature extraction, while optimization-based methods (also known as keyframe-based approaches) rely on global optimizations, which increase the systems accuracy, as well as the algorithms computational cost. SLAM systems based on RGB-D data started to attract more attention with the advent of Microsofts Kinect in 2010. ; Aziz, M.I. and F.C. In Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan, 2528 November 2020; pp. Zhan, Z.; Jian, W.; Li, Y.; Yue, Y. ** Embedded Implementation. Covolan, J.P.; Sementille, A.; Sanches, S. A mapping of visual SLAM algorithms and their applications in augmented reality. A method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Computing methodologies. [. Xiaogang, R.; Wenjing, Y.; Jing, H.; Peiyuan, G.; Wei, G. Monocular Depth Estimation Based on Deep Learning: A Survey. Among all the SLAM algorithms in the literature, it is essential to achieve a fair comparison between them to determine which one presents a better performance in certain situations. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large . Available online: Aslam, M.S. ; Zhang, T.; Gao, X.; Wang, D.; Xian, Y. Semi-direct monocular visual and visual-inertial SLAM with loop closure detection. [. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. Andra Macario Barros, Maugan Michel, Yoann Moline, Gwenol Corre, Frdrick Carrel. You can download the paper by clicking the button above. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. Multiple requests from the same IP address are counted as one view. This way, the IMU is capable of providing information relative to the angular rate (gyroscope) and acceleration (accelerometer) along the. In this study, the algorithm adopts only a monocular camera and multiple pictures to rebuild the map and estimate localization and the attitude of the camera. ; Roumeliotis, S.I. ; Yang, S.; Li, R. An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture. Campos, C.; Elvira, R.; Rodrguez, J.J.G. [, Singandhupe, A.; La, H. A Review of SLAM Techniques and Security in Autonomous Driving. ; Pinto, J.B.N.G. [, Bloesch, M.; Omari, S.; Hutter, M.; Siegwart, R. Robust visual inertial odometry using a direct EKF-based approach. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors' pose estimation in an unknown environment. In Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan, 1316 November 2007; pp. Crowd-SLAM: Visual SLAM Towards Crowded Environments using Object Detection. This paper, firstly, discusses two popular existing visual odometry approaches, namely LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM systems using Umeyama Method. They present a higher technical difficulty due to their limited visual input [, To obtain a general overview and an introduction to the SLAM problem, the work by Durrant-White and Bailey [. SLAM++: Simultaneous Localisation and Mapping at the Level of Objects. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing featureless (direct) method matching with accurate semi-dense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient. Simultaneous localization and mapping (SLAM) technology, first proposed by Smith in 1986 [, The map construction comes with two other tasks: localization and path planning. Paul, M.K. ; Amiri, A.; Mashohor, S.; Tang, S.; Zhang, H. CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction. Furthermore, it requires the users interaction to establish the initial keyframes, and it presents a non-negligible power consumption, which makes it unsuitable for low-cost embedded systems [, Dense tracking and mapping (DTAM), proposed by Newcombe et al. Inertial-Only Optimization for Visual-Inertial Initialization. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2024 May 2019; pp. In the feature-based algorithms relying on filters (filtering-based algorithms), the first step consists of initializing the map points with high uncertainty, which may converge later to their actual positions. 91909197. Xu, Q.; Chavez, A.G.; Blow, H.; Birk, A.; Schwertfeger, S. Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-Cameras. [, Vincke, B.; Elouardi, A.; Lambert, A.; Merigot, A. Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors' pose estimation in an unknown environment. The best SLAM algorithm shall be selected after considering the variety of features and specificities that this environment and application possess. In Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 613 November 2011; pp. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees . Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. Jinyu, L.; Bangbang, Y.; Danpeng, C.; Nan, W.; Guofeng, Z.; Hujun, B. ; Nerurkar, E.D. 9921000. The authors declare no conflict of interest. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. This Section presented seven main visual-inertial SLAM algorithms, as long as an individual analysis of each of them. After the images acquisition from more than one point of view, the system performs the initialization process to define a global coordinate system and reconstruct an initial map. 21002106. ; Wagstaff, H.; Shenoy, G.S. Furthermore, we propose six criteria that ease the SLAM algorithms analysis and consider both the software and hardware levels. One important and recent study in this area is presented in [, Research studies into the SLAM algorithms considering dynamic environments are essential to increase the algorithms robustness to more realistic situations. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 15 October 2018; pp. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. In Proceedings of the IROS 2020IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA, 2529 October 2020; pp. A real-time visual SLAM is proposed by Davison . Sun, Y.; Liu, M.; Meng, M.Q.H. Algorithm type: this criterion indicates the methodology adopted by the algorithm. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. Conceptualization, A.M.B., M.M., Y.M., G.C. This work investigated the main algorithms of visual SLAM, and its applications in augmented reality, and described the key features of these algorithms and two taxonomies for SLAM techniques are proposed. A general framework is developed and consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. ; Molton, N.D.; Stasse, O. MonoSLAM: Real-Time Single Camera SLAM. The visual-only SLAM category can be divided into two main methods: feature-based and direct. [. Bianco, S.; Ciocca, G.; Marelli, D. Evaluating the Performance of Structure from Motion Pipelines. [. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. Please let us know what you think of our products and services. Global optimization: SLAM algorithms may include global map optimization, which refers to the technique that searches to compensate the accumulative error introduced by the camera movement, considering the consistency of the entire structure. No special In order to be human-readable, please install an RSS reader. [, Jaenal, A.; Zuiga-Nel, D.; Gomez-Ojeda, R.; Gonzalez-Jimenez, J. [. [. Mur-Artal, R.; Montiel, J.; Tardos, J. ORB-SLAM: A versatile and accurate monocular SLAM system. [, Salas-Moreno, R.F. Cao, Y.; Hu, L.; Kneip, L. Representations and Benchmarking of Modern Visual SLAM Systems. In Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality, Orlando, FL, USA, 1922 October 2009; pp. 165172. 431437. The selected visual-only SLAM algorithms are presented in, The first monocular SLAM algorithm is MonoSLAM, which was proposed by Davidson et al. [, Merzlyakov, A.; Macenski, S. A Comparison of Modern General-Purpose Visual SLAM Approaches. ; Neira, J. DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM. and F.C. [, Scona, R.; Jaimez, M.; Petillot, Y.R. Gui, J.; Gu, D.; Wang, S.; Hu, H. A review of visual inertial odometry from filtering and optimisation perspectives. [, The semi-direct visual odometry (SVO) algorithm [, The large-scale direct monocular SLAM (LSD-SLAM) [, This algorithm does not suffer from absolute scale limitation, since it uses depth prediction to perform the scale estimation [, The direct sparse odometry (DSO) algorithm [. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. 1 A Survey on Long-Tailed Visual Recognition . 35653572. 5769. [, Jin, Q.; Liu, Y.; Man, Y.; Li, F. Visual SLAM with RGB-D Cameras. Taketomi, T.; Uchiyama, H.; Ikeda, S. Visual SLAM algorithms: A survey from 2010 to 2016. A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges. We describe methods to raycast point clouds from the volume using virtual cameras, and use the point clouds for heightmaps generation (e.g., useful for locomotion) or object dense point cloud extraction (e.g., useful for manipulation). ; Roumeliotis, S.I. ; Gonzalez-Jimenez, J. Bescos, B.; Campos, C.; Tards, J.D. Silveira, O.C.B. Photos used throughout the site by David Jorre, Jean-Philippe Delberghe, JJ Ying, Luca Bravo, Brandi Redd, & Christian Perner from Unsplash. In this paper, we introduced the main visual-based SLAM approaches and a brief description and systematic analyses of a set of the most exemplary techniques of each approach. ; Siegwart, R. A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM. Visual-based SLAM techniques play a significant role in [. [, Although the SLAM domain has been widely studied for years, there are still several open problems. The current state of the art of SLAM and odometry algorithms increasingly seeks to reinforce the algorithms robustness, optimize computational resources usage, and evolve the environments understanding in the map representations [, Another main issue that decreases the SLAM algorithms robustness is the assumption of static scenarios, while the real world presents dynamic environments; this may cause failures in tracking [, Besides the robustness, recent SLAM algorithms seek to consider the usage of the computational resources [, Currently, the SLAM algorithms also seek to evolve our understanding of the environment in the performed reconstructions [, One remarkable algorithm that incorporates deep learning concepts is the UnDeepVO [, Another relevant algorithm based on deep learning is the DF-SLAM [, Incorporating semantic information on the visual-SLAM problem is a growing field, and has been attracting more attention in recent years. . The visual-based approaches can be divided into three main categories: visual-only SLAM, visual-inertial (VI) SLAM, and RGB-D SLAM. Editors Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 37 November 2013; pp. ; Montiel, J.M.M. 1522. This feature-based SLAM technique is the basis of modern SLAM for real time applications. We describe methods to raycast point clouds from the volume using virtual cameras, and use the point clouds for heightmaps generation (e.g., useful for locomotion) or object dense point cloud extraction (e.g., useful for manipulation). In Proceedings of the 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Fayetteville, AR, USA, 36 May 2020; pp. The visual-only SLAM systems are based on 2D image processing. Doctoral Dissertation, Iowa State University, Ames, IA, USA, 2017. ; Rodrigues, L.R.L. Loop closure: the loop closing detection refers to the capability of the SLAM algorithm to identify the images that were previously detected by the algorithm to estimate and correct the drift accumulated during the sensor movement. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. Editors select a small number of articles recently published in the journal that they believe will be particularly This section presents concepts related to visual-based SLAM and odometry algorithms, and the main characteristics of the visual-based approaches covered in this paper. 73227328. Vincke, B.; Elouardi, A.; Lambert, A. Soares, J.C.V. [. There are many different algorithms based on this methodology, and depending on the chosen technique, the reconstruction may be dense, semi-dense, or sparse. Smith, R.; Cheeseman, P. On the Representation and Estimation of Spatial Uncertainty. 14. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May31 August 2020; pp. Durrant-Whyte, H.; Bailey, T. Simultaneous localization and mapping: Part I. Bailey, T.; Durrant-Whyte, H. Simultaneous localization and mapping (SLAM): Part II. Furthermore, we propose six criteria that ease the SLAM algorithms analysis and consider both the software and hardware levels. International Symposium on Experimental Robotics, Surveying and Geospatial Engineering Journal, 2017 IEEE International Conference on Robotics and Automation (ICRA), 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IJAIT (International Journal of Applied Information Technology), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Image Analysis and Processing ICIAP 2019, 2016 4th International Conference on Robotics and Mechatronics (ICROM), 2018 IEEE International Conference on Robotics and Automation (ICRA), Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle, Combining Feature-based and Direct Methods for Semi-dense Real-time Stereo Visual Odometry, Visual Simultaneous Localization and Mapping: A Survey Precision Agriculture using Drones and Image Processing View project, Ultra-Wideband Aided Localization and Mapping System, Efficient Multi-Camera Visual-Inertial SLAM for Micro Aerial Vehicles, Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots, EVALUATION OF THE VISUAL ODOMETRY METHODS FOR SEMI-DENSE REAL-TIME, rxKinFu: Moving Volume KinectFusion for 3D Perception and Robotics, Experimental Comparison of open source Vision based State Estimation Algorithms, Coded grouping-based inspection algorithms to detect malicious meters in neighborhood area smart grid, Real-time dense map fusion for stereo SLAM, Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review, An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid, A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision, S-PTAM: Stereo Parallel Tracking and Mapping, The Simultaneous Localization and Mapping (SLAM)-An Overview, Self-Calibration and Visual SLAM with a Multi-Camera System on a Micro Aerial Vehicle, VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems, Point-Line Visual Stereo SLAM Using EDlines and PL-BoW, GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm, Real-time local 3D reconstruction for aerial inspection using superpixel expansion, Feature-based visual odometry prior for real-time semi-dense stereo SLAM, Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking, Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM, Feature-based visual simultaneous localization and mapping: a survey, Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain, Autonomous Flight and Real-Time Tracking of Unmanned Aerial Vehicle, Deep Learning for Visual SLAM in Transportation Robotics: A review, Keyframe-Based Photometric Online Calibration and Color Correction, RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments, SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor, Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor, Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review, Evaluation of the Robustness of Visual SLAM Methods in Different Environments, SWIR Camera-Based Localization and Mapping in Challenging Environments, Autonomous flight and obstacle avoidance of a quadrotor by monocular SLAM, The MADMAX data set for visual-inertial rover navigation on Mars, Multi-Modal Loop Closing in Unstructured Planetary Environments with Visually Enriched Submaps, Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions, Outdoor obstacle avoidance based on hybrid visual stereo SLAM for an autonomous quadrotor MAV, From SLAM to Situational Awareness: Challenges and Survey, SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM, Combining SLAM with muti-spectral photometric stereo for real-time dense 3D reconstruction, PRGFlow: Benchmarking SWAP-Aware Unified Deep Visual Inertial Odometry. 828835. Davison, A.J. [, Klein, G.; Murray, D. Parallel Tracking and Mapping for Small AR Workspaces. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. Evaluation of a SoC for Real-time 3D SLAM. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. In the following, we present the selected SLAM algorithms considered the most representative of each of the three presented approaches according to their publication years. Design and evaluation of an embedded system based SLAM applications. Ruan, K.; Wu, Z.; Xu, Q. This website uses cookies to ensure you get the best experience. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May7 June 2014; pp. 3D registration (i.e., accurate pose registration/localization) is the key fundamental technique for achieving immersive AR effects. Semi-dense SLAM on an FPGA SoC. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. ; Tards, J.D. ; Newcombe, R.A.; Strasdat, H.; Kelly, P.H. Regarding future works, we will apply the proposed criteria analysis to nuclear decommissioning scenarios. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing featureless (direct) method matching with accurate semi-dense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient. As the RGB-D devices directly provide the depth map to the SLAM systems, the general framework of SLAM based on this approach differs from the other ones already presented. Taketomi T Uchiyama H Ikeda S Visual slam algorithms: a survey from 2010 to 2016 IPSJ Trans. Help us to further improve by taking part in this short 5 minute survey, Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces, A New Hyperloop Transportation System: Design and Practical Integration, https://www.doc.ic.ac.uk/~ajd/Scene/index.html, https://www.xilinx.com/products/intellectual-property/dpu.html#overview, https://github.com/daniilidis-group/msckf_mono, https://github.com/KumarRobotics/msckf_vio, https://github.com/HKUST-Aerial-Robotics/VINS-Mono, https://github.com/RonaldSun/VI-Stereo-DSO, https://github.com/ParikaGoel/KinectFusion, https://vision.in.tum.de/data/datasets/rgbd-dataset, https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html, https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets, https://vision.in.tum.de/data/datasets/visual-inertial-dataset, https://creativecommons.org/licenses/by/4.0/. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May3 June 2017; pp. In addition, we presented some major issues, suggested future directions for the field, and discussed the main benchmarking datasets for visual-SLAM and odometry algorithms evaluation. An in-depth literature survey of forty-two impactful papers published in the domain of VSLAMs is given, including the novelty domain, objectives, employed algorithms, and semantic level, and discusses the current trends and future directions that may help researchers investigate them. can be divided into three main categories: visual-only SLAM, visual-inertial (VI) SLAM, and RGB-D SLAM. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. RGB-D sensors consist of a monocular RGB camera and a depth sensor, allowing SLAM systems to directly acquire the depth information with a feasible accuracy accomplished in real-time by low-cost hardware. https://doi.org/10.3390/robotics11010024. 13 A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key! 5157. In Proceedings of the 2020 22nd Symposium on Virtual and Augmented Reality (SVR), Porto de Galinhas, Brazil, 710 November 2020. 1996-2022 MDPI (Basel, Switzerland) unless otherwise stated. Thus, this paper provides a review of the most representative visual-based SLAM techniques and an overview of each methods main advantages and disadvantages. Another pioneer algorithm is the Parallel Tracking and Mapping (PTAM) [, PTAM allows the map representation by a large number of features and performs global optimization. Despite these advantages, the PTAM algorithm presents a high complexity due to the bundle adjustment step. MDPI. The other mapping thread integrates the visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that a bundle adjustment is performed to provide robust trajectory estimation. However, the feature extraction may fail in a textureless environment [, In contrast with the feature-based methods, the direct methods use the sensor data without pre-processing, considering pixels intensities, and minimizing the photometric error. Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 1215 March 2018; pp. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. Fuentes-Pacheco, J.; Ruiz-Ascencio, J.; Rendn-Mancha, J.M. ; Reid, I.D. In Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, 1014 April 2007; pp. Ondrka, P.; Kohli, P.; Izadi, S. MobileFusion: Real-Time Volumetric Surface Reconstruction and Dense Tracking on Mobile Phones. https://doi.org/10.3390/robotics11010024, Macario Barros, Andra, Maugan Michel, Yoann Moline, Gwenol Corre, and Frdrick Carrel. In this Section, we presented the main visual-only-based SLAM algorithms. This algorithm neither employs global optimization techniques nor loop closure detection. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM . Chang, L.; Niu, X.; Liu, T. GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration. In Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 57 November 2020; pp. You are accessing a machine-readable page. Advanced Computing: An International Journal ( ACIJ ). most exciting work published in the various research areas of the journal. Academia.edu no longer supports Internet Explorer. Li, R.; Wang, S.; Gu, D. DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning. Available online. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. [. In general, the visual-based SLAM algorithms are divided into three main threads: initialization, tracking, and mapping [, As one can see in the Figure, in visual-SLAM systems, the input can be a 2D image, both a 2D image and IMU data, or a 2D image and depth data, depending on the used approach, i.e., visual-only (, Although we mainly refer to the concepts as belonging to the SLAM methodology, we consider, in this paper, both visual-SLAM and visual-odometry (VO) techniques, since they are closely related. Embedded implementations: the embedded SLAM implementation is an emerging field used in several applications, especially in robotics and automobile domains. [, Campos, C.; Montiel, J.M. Feature Papers represent the most advanced research with significant potential for high impact in the field. In addition, it does not count with loop closure, and the generated map is more suitable to identify landmarks. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 15 October 2018. A comprehensive overview of dynamic visual SLAM and deep learning: concepts, methods and challenges. Lastly, the RGB-D approach can be divided concerning their tracking method, which can be direct, hybrid, or feature-based. In Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), New Orleans, LA, USA, 1822 May 2020; pp. Moreover, we present different methods for keeping the camera fixed with respect to the moving volume, fusing also IMU data and the camera heading/velocity estimation. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. RGB-D SLAM Dataset and Benchmark. Here, we present the publicly available benchmark dataset used to evaluate the presented SLAM algorithms in their original articles. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. We assembled the main publications we found presenting fully embedded SLAM systems in platforms such as microcontrollers and FPGA boards. ; Greenspan, M.A. All articles published by MDPI are made immediately available worldwide under an open access license. In Proceedings of the 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT), Haifa, Israel, 1115 September 2016; pp. Dai, W.; Zhang, Y.; Li, P.; Fang, Z.; Scherer, S. RGB-D SLAM in Dynamic Environments Using Point Correlations. A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph. Canovas, B.; Rombaut, M.; Ngre, A.; Pellerin, D.; Olympieff, S. Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes. Sorry, preview is currently unavailable. ; supervision, M.M., Y.M., G.C. [. Bresson, G.; Alsayed, Z.; Yu, L.; Glaser, S. Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving. ; investigation, A.M.B. ; Davison, A.J. To guide the choices among all the algorithms, we proposed six criteria that are limiting factors to several SLAM projects: the algorithm type, the density of the reconstructed map, the presence of global optimizations and loop closures techniques, its availability, and the embedded implementations already performed. ; Fallon, M.; Cremers, D. StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. Academia.edu no longer supports Internet Explorer. Engel, J.; Koltun, V.; Cremers, D. Direct Sparse Odometry. Abouzahir, M.; Elouardi, A.; Latif, R.; Bouaziz, S.; Tajer, A. Embedding SLAM algorithms: Has it come of age? Author to whom correspondence should be addressed. Ming, Y.; Meng, X.; Fan, C.; Yu, H. Deep learning for monocular depth estimation: A review. 602607. ; Kumar, V. Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight. Wan, Z.; Yu, B.; Li, T.; Tang, J.; Wang, Y.; Raychowdhury, A.; Liu, S. A Survey of FPGA-Based Robotic Computing. In general, they construct dense maps, enabling them to represent the environment in greater detail. ; Davison, A.J. [, Newcombe, R.A.; Izadi, S.; Hilliges, O.; Molyneaux, D.; Kim, D.; Davison, A.J. [. several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest Find support for a specific problem in the support section of our website. [. Enter the email address you signed up with and we'll email you a reset link. Mono-SLAM is a V-SLAM technique for real time application which is developed by Davison (2003 ). In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. [, Nardi, L.; Bodin, B.; Zia, M.Z. In addition, VI-SLAM algorithms present different implementations according to their back-end approach, which can be filtering-based or optimization-based. The literature presents different approaches and methods to implement visual-based SLAM systems. 135140. Simultaneously, the mapping process includes new points in the 3D reconstruction as more unknown scenes are observed. Available online: Piat, J.; Fillatreau, P.; Tortei, D.; Brenot, F.; Devy, M. HW/SW co-design of a visual SLAM application. Abstract. A general framework is developed and consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Kabzan, J.; Valls, M.; Reijgwart, V.; Hendrikx, H.; Ehmke, C.; Prajapat, M.; Bhler, A.; Gosala, N.; Gupta, M.; Sivanesan, R.; et al. Abstract: SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Content on this site is licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. The rst one refers to the SLAM techniques based only on 2D images provided by a. RGB-D systems present advantages such as providing color image data and dense depth map without any pre-processing step, hence decreasing the complexity of the SLAM initialization [. and F.C. Several benchmarking datasets with different characteristics are proposed in the literature to explore the SLAM capabilities and robustness. [, Williams, B. ; methodology, A.M.B., M.M. Qin, T.; Li, P.; Shen, S. VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator. KinectFusion is an impressive algorithm that was introduced in 2011 to simultaneously track the movement of a depth camera in the 3D space and densely reconstruct the environment as a Truncated Signed Distance Formula (TSDF) volume, in real-time. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors' pose estimation in an unknown environment. Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps. 3337. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. Availability: several SLAM algorithms are open source and made available by the authors or have their implementations made available by third parties, facilitating their usage and reproduction. Lepetit, V.; Moreno-Noguer, F.; Fua, P. EPnP: An Accurate O(n) Solution to the PnP Problem. [, Kerl, C.; Sturm, J.; Cremers, D. Dense visual SLAM for RGB-D cameras. 171179. Li, R.; Wang, S.; Long, Z.; Gu, D. UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning. [. Vis. We release the code as an open-source package, using the Robotic Operating System (ROS) and the Point Cloud Library (PCL). Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. Firstly, in [, An essential algorithm robust to dynamic scenes is the Dynamic-SLAM proposed by Xiao et al. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. We use cookies on our website to ensure you get the best experience. Improving RGB-D SLAM in dynamic environments: A motion removal approach. Chen, K.; Lai, Y.; Hu, S. 3D indoor scene modeling from RGB-D data: A survey. The visual-based SLAM techniques use one or more cameras in the sensor system, receiving 2D images as the source of information. [. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Advanced Computing: An International Journal ( ACIJ ). Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. ; Rosa, P.F.F. In a general analysis, the addition of an IMU to visual-based SLAM algorithms has the primary purpose of increasing the systems robustness, which was already demonstrated to be true [, The most representative SLAM algorithms based on RGB-D sensors, i.e., considering RGB images and depth information directly, are presented in, The dense visual odometry SLAM (DVO-SLAM) algorithm, proposed by Kerl et al. 2. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. In this work, we further develop the Moving Volume KinectFusion method (as rxKinFu) to fit better to robotic and perception applications, especially for locomotion and manipulation tasks. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction. The literature presents different approaches and methods to implement visual-based SLAM systems. Liu, C.; Zhou, C.; Cao, W.; Li, F.; Jia, P. A Novel Design and Implementation of Autonomous Robotic Car Based on ROS in Indoor Scenario. A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. 49965001. 57835790. Beshaw et al. ; formal analysis, A.M.B., M.M., Y.M. In Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland, 29 August2 September 2016; pp. The first one refers to the SLAM techniques based only on 2D images provided by a monocular or stereo camera. articles published under an open access Creative Common CC BY license, any part of the article may be reused without The reconstruction density is a substantial constraint to the algorithms real-time operation, since the joint optimization of both structure and camera positions is more computationally expensive for dense and semi-dense reconstructions than for a sparse one [, The VI-SLAM approach incorporates inertial measurements to estimate the structure and the sensor pose. Xu, Q.; Kuang, H.; Kneip, L.; Schwertfeger, S. Rethinking the Fourier-Mellin Transform: Multiple Depths in the Cameras View. "A Comprehensive Survey of Visual SLAM Algorithms" Robotics 11, no. Serrata, A.A.J. [. ; D. Tards, J. ORB-SLAM3: An Accurate Open-Source Library for Visual, VisualInertial, and Multimap SLAM. [, Li, M.; Mourikis, A.I. Boikos, K.; Bouganis, C.S. permission is required to reuse all or part of the article published by MDPI, including figures and tables. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. Visual simultaneous localization and mapping: A survey. [, Mourikis, A.I. 530535. A Comprehensive Survey of Visual SLAM Algorithms. Handa, A.; Whelan, T.; McDonald, J.; Davison, A.J. Petit, B.; Guillemard, R.; Gay-Bellile, V. Time Shifted IMU Preintegration for Temporal Calibration in Incremental Visual-Inertial Initialization. Last, we integrate and show some demonstrations of rxKinFu on the mini-bipedal robot RPBP, our wheeled quadrupedal robot CENTAURO, and the newly developed full-size humanoid robot COMAN+. In addition, the algorithms complexity increases proportionally with the size of the environment. Forster, C.; Pizzoli, M.; Scaramuzza, D. SVO: Fast semi-direct monocular visual odometry. ; writingoriginal draft preparation, A.M.B. interesting to readers, or important in the respective research area. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May7 June 2014; pp. (2022) Macario Barros et al. Robotics, 11 (1):24, 2022. Integrating algorithmic parameters into benchmarking and design space exploration in 3D scene understanding. ; Davison, A.J. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. ; de Melo, J.G.O.C. progress in the field that systematically reviews the most exciting advances in scientific literature. https://doi.org/10.3390/robotics11010024, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. 24192425. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. Visual SLAM algorithms: a survey from 2010 to 2016. Leutenegger, S.; Lynen, S.; Bosse, M.; Siegwart, R.; Furgale, P. Keyframe-Based Visual-Inertial Odometry Using Nonlinear Optimization. [, Von Stumberg, L.; Usenko, V.; Cremers, D. Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. Sun, K.; Mohta, K.; Pfrommer, B.; Watterson, M.; Liu, S.; Mulgaonkar, Y.; Taylor, C.J. Yu, J.; Gao, F.; Cao, J.; Yu, C.; Zhang, Z.; Huang, Z.; Wang, Y.; Yang, H. CNN-based Monocular Decentralized SLAM on embedded FPGA. This research received no external funding. DTAM: Dense tracking and mapping in real-time. Authors. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). This procedure is followed by tracking, which attempts to estimate the camera pose. 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