In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan, 27 September4 October 2009; pp. methods, instructions or products referred to in the content. A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph. In Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2225 September 2019; pp. 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. Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review 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. ; Naveed, K.; uz Zaman, U.K. An RPLiDAR based SLAM equipped with IMU for Autonomous Navigation of Wheeled Mobile Robot. Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM. 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. Petit, B.; Guillemard, R.; Gay-Bellile, V. Time Shifted IMU Preintegration for Temporal Calibration in Incremental Visual-Inertial Initialization. Xiaogang, R.; Wenjing, Y.; Jing, H.; Peiyuan, G.; Wei, G. Monocular Depth Estimation Based on Deep Learning: A Survey. Engel, J.; Koltun, V.; Cremers, D. Direct Sparse Odometry. 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. 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. This feature-based SLAM technique is the basis of modern SLAM for real time applications. 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. ; writingoriginal draft preparation, A.M.B. The visual-based approaches can be divided into three main categories: visual-only SLAM, visual-inertial (VI) SLAM, and RGB-D SLAM. 14491456. Feature Papers represent the most advanced research with significant potential for high impact in the field. 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. [, Newcombe, R.A.; Izadi, S.; Hilliges, O.; Molyneaux, D.; Kim, D.; Davison, A.J. Soares, J.C.V. Editors select a small number of articles recently published in the journal that they believe will be particularly Paul, M.K. RGB-D SLAM Dataset and Benchmark. 171179. In addition, it is a more robust approach regarding low-texture environments thanks to the depth sensor. 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. 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. [doi] Abstract. [, Jin, Q.; Liu, Y.; Man, Y.; Li, F. Visual SLAM with RGB-D Cameras. ; Gattass, M.; Meggiolaro, M.A. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large . 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. 65656574. Bianco, S.; Ciocca, G.; Marelli, D. Evaluating the Performance of Structure from Motion Pipelines. 2006. [. Advanced Computing: An International Journal ( ACIJ ). Smart Cleaner: A New Autonomous Indoor Disinfection Robot for Combating the COVID-19 Pandemic. ; Amiri, A.; Mashohor, S.; Tang, S.; Zhang, H. CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction. 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. 49584965. You can download the paper by clicking the button above. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2529 October 2020; pp. 3337. 49965001. Embedded implementations: the embedded SLAM implementation is an emerging field used in several applications, especially in robotics and automobile domains. 3D registration (i.e., accurate pose registration/localization) is the key fundamental technique for achieving immersive AR effects. Some popular SLAM methods including ORB-SLAM [7,8,9], LSD-SLAM , and DSO-SLAM have been developed in these years. Crowd-SLAM: Visual SLAM Towards Crowded Environments using Object Detection. ; Xie, L. VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM. 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. Robotics. [. 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. [. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. prior to publication. ; Riley, G.D.; et al. Taketomi T Uchiyama H Ikeda S Visual slam algorithms: a survey from 2010 to 2016 IPSJ Trans. Evaluation of a SoC for Real-time 3D SLAM. 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. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. 46794685. ; Roumeliotis, S.I. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. Bescos, B.; Campos, C.; Tards, J.D. [, Singandhupe, A.; La, H. A Review of SLAM Techniques and Security in Autonomous Driving. Visual-based SLAM techniques play a significant role in this field,. The Feature Paper can be either an original research article, a substantial novel research study that often involves [. Appl. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, ; Wu, K.; Hesch, J.A. Yousif, K.; Bab-Hadiashar, A.; Hoseinnezhad, R. An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics. In Proceedings of the 2014 International Conference on Field-Programmable Technology (FPT), Shanghai, China, 1012 December 2014; pp. The term visual SLAM defines the problem of build a map of an environment and perform location, simultaneously. All articles published by MDPI are made immediately available worldwide under an open access license. ; Moreira, L.A.S. Photos used throughout the site by David Jorre, Jean-Philippe Delberghe, JJ Ying, Luca Bravo, Brandi Redd, & Christian Perner from Unsplash. All authors have read and agreed to the published version of the manuscript. Robotics. 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. ; Siegwart, R. A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM. 2. In 2012, we introduced the Moving Volume KinectFusion method that allows the volume/camera move freely in the space. 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. 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. 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. 2As far as we know, this is the first review article that presents the three main visual-based approaches, performing an individual analysis of each method and a general analysis of the approaches. interesting to readers, or important in the respective research area. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. 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. Robotics 2022, 11, 24. Despite these advantages, the PTAM algorithm presents a high complexity due to the bundle adjustment step. 24362440. ; Molton, N.D.; Stasse, O. MonoSLAM: Real-Time Single Camera SLAM. 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. ; Aziz, M.I. Firstly, in [, An essential algorithm robust to dynamic scenes is the Dynamic-SLAM proposed by Xiao et al. Delmerico, J.; Scaramuzza, D. A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. Davison, A.J. In the augmented reality experience, we can apply SLAM techniques to insert . Beshaw et al. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2024 May 2019; pp. Jinyu, L.; Bangbang, Y.; Danpeng, C.; Nan, W.; Guofeng, Z.; Hujun, B. ; Yang, S.; Li, R. An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture. Regarding future works, we will apply the proposed criteria analysis to nuclear decommissioning scenarios. 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 . Zhan, Z.; Jian, W.; Li, Y.; Yue, Y. 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+. SLAM algorithms based on features consider a certain number of points of interest, called keypoints. 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. Endres, F.; Hess, J.; Sturm, J.; Cremers, D.; Burgard, W. 3-D Mapping With an RGB-D Camera. 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. The embedded implementations presented in, A timeline representing the selected visual-inertial algorithms is presented in, The multi-state constraint Kalman filter (MSCKF) [, Open Keyframe-based Visual-Inertial SLAM (OKVIS) [, The Robust Visual Inertial Odometry (ROVIO) algorithm [, The Visual-Inertial ORB-SLAM (VIORB) algorithm [, Monocular Visual-Inertial System (VINS-Mono) [, The Visual-Inertial Direct Sparse Odometry (VI-DSO) algorithm [, The already mentioned ORB-SLAM3 algorithm [. 326329. In Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan, 2528 November 2020; pp. 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. Silveira, O.C.B. Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. Mur-Artal, R.; Tards, J.D. 72337238. Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, CC0 1.0 Universal (CC0) Public Domain Dedication. Another main benchmark dataset is the ICL-NUIM [, A dataset commonly used to evaluate monocular systems is the TUM MonoVO [. 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. (2022) Macario Barros et al. Author to whom correspondence should be addressed. Belshaw, M.S. ; Rosa, P.F.F. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 48 January 2022; pp. [, Nardi, L.; Bodin, B.; Zia, M.Z. [, Seiskari, O.; Rantalankila, P.; Kannala, J.; Ylilammi, J.; Rahtu, E.; Solin, A. HybVIO: Pushing the Limits of Real-Time Visual-Inertial Odometry. Covolan, J.P.; Sementille, A.; Sanches, S. A mapping of visual SLAM algorithms and their applications in augmented reality. ; Tards, J.D. Zhang, S.; Zheng, L.; Tao, W. Survey and Evaluation of RGB-D SLAM. 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 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2328 June 2013; pp. Van Opdenbosch, D.; Aykut, T.; Alt, N.; Steinbach, E. Efficient Map Compression for Collaborative Visual SLAM. Macario Barros, A.; Michel, M.; Moline, Y.; Corre, G.; Carrel, F. A Comprehensive Survey of Visual SLAM Algorithms. Davison, A.J. [, Gao, X.; Wang, R.; Demmel, N.; Cremers, D. LDSO: Direct Sparse Odometry with Loop Closure. Save time finding and organizing research with Mendeley. ; Mawer, J.; Nisbet, A.; Kelly, P.H.J. 1522. AMZ Driverless: The Full Autonomous Racing System. Deng, X.; Zhang, Z.; Sintov, A.; Huang, J.; Bretl, T. Feature-constrained Active Visual SLAM for Mobile Robot Navigation. Available online: Visual-Inertial Dataset. ; Neira, J. DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM. Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. 15241531. 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. https://doi.org/10.3390/robotics11010024, Macario Barros A, Michel M, Moline Y, Corre G, Carrel F. A Comprehensive Survey of Visual SLAM Algorithms. Taketomi, T.; Uchiyama, H.; Ikeda, S. Visual SLAM algorithms: A survey from 2010 to 2016. This paper covers topics from the basic SLAM methods, vision sensors, machine vision algorithms for feature extraction and matching, Deep Learning (DL) methods and datasets for Visual Odometry (VO) and Loop Closure (LC) in V-SLAM applications. Inertial-Only Optimization for Visual-Inertial Initialization. Ondrka, P.; Kohli, P.; Izadi, S. MobileFusion: Real-Time Volumetric Surface Reconstruction and Dense Tracking on Mobile Phones. You can download the paper by clicking the button above. Qin, T.; Li, P.; Shen, S. VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator. MDPI. ; methodology, A.M.B., M.M. A high-performance system-on-chip architecture for direct tracking for SLAM. 72867291. [, Although the SLAM domain has been widely studied for years, there are still several open problems. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 15 October 2018. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. Available online. 95729582. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. A real-time visual SLAM is proposed by Davison . In Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2630 May 2015; pp. A Comprehensive Survey of Visual SLAM Algorithms. ; de Melo, J.G.O.C. Abouzahir, M.; Elouardi, A.; Latif, R.; Bouaziz, S.; Tajer, A. Embedding SLAM algorithms: Has it come of age? 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. 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. We use cookies on our website to ensure you get the best experience. [, Mourikis, A.I. Leutenegger, S.; Lynen, S.; Bosse, M.; Siegwart, R.; Furgale, P. Keyframe-Based Visual-Inertial Odometry Using Nonlinear Optimization. In Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, The Netherlands, 1719 June 2020; pp. Simultaneously, the mapping process includes new points in the 3D reconstruction as more unknown scenes are observed. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. There are many different algorithms based on this methodology, and depending on the chosen technique, the reconstruction may be dense, semi-dense, or sparse. [, Kerl, C.; Sturm, J.; Cremers, D. Dense visual SLAM for RGB-D cameras. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. 2022. The literature presents different approaches and methods to implement visual-based SLAM systems. Canovas, B.; Rombaut, M.; Ngre, A.; Pellerin, D.; Olympieff, S. Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes. Burri, M.; Nikolic, J.; Gohl, P.; Schneider, T.; Rehder, J.; Omari, S.; Achtelik, M.; Siegwart, R. The EuRoC micro aerial vehicle datasets. 530535. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May3 June 2017; pp. 225234. 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. and F.C. Previous Article in Journal. 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. In Proceedings of the 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Fayetteville, AR, USA, 36 May 2020; pp. In Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 57 November 2020; pp. In Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland, 29 August2 September 2016; pp. 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. 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. 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. Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. 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. 23202327. However, Visual-SLAM is known to be resource-intensive in memory and processing time. * Global Optimization. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 37 November 2013; pp. Nguyen, T.M. [. This research received no external funding. MDPI and/or Available online: Piat, J.; Fillatreau, P.; Tortei, D.; Brenot, F.; Devy, M. HW/SW co-design of a visual SLAM application. An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems. 573580. This way, the IMU is capable of providing information relative to the angular rate (gyroscope) and acceleration (accelerometer) along the. [. 2022; 11(1):24. Thus, this paper provides a review of the most representative visual-based SLAM techniques and an overview of each methods main advantages and disadvantages. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 1215 March 2018; pp. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Efficient implementation of EKF-SLAM on a multi-core embedded system. SLAM++: Simultaneous Localisation and Mapping at the Level of Objects. Integrating algorithmic parameters into benchmarking and design space exploration in 3D scene understanding. Survey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. Campos, C.; Elvira, R.; Rodrguez, J.J.G. The monocular camera-based SLAM is a well-explored domain given the small size of the sensor (the smallest of all the presented approaches), its low price, easy calibration, and reduced power consumption [. The main benefits and drawbacks of each method were individually addressed. [, Soares, J.C.V. Visual SLAM algorithms: a survey from 2010 to 2016. [, Merzlyakov, A.; Macenski, S. A Comparison of Modern General-Purpose Visual SLAM Approaches. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM . 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. 8386. In Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality, Orlando, FL, USA, 1922 October 2009; pp. ; Gonzalez-Jimenez, J. paper provides an outlook on future directions of research or possible applications. The visual-only SLAM system may use a monocular or stereo camera. [, Salas-Moreno, R.F. ; Kumar, V. Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight. In addition, the algorithms complexity increases proportionally with the size of the environment. Gait recognition aims at identifying a person at a distance through visual cameras. (2022) Macario Barros et al. [. ; Montiel, J.M.M. Visit our dedicated information section to learn more about MDPI. 1: 24. A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May31 August 2020; pp. https://doi.org/10.3390/robotics11010024, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. In. The selected visual-only SLAM algorithms are presented in, The first monocular SLAM algorithm is MonoSLAM, which was proposed by Davidson et al. [, Williams, B. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. 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. Available online: Aslam, M.S. In Proceedings of the 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT), Haifa, Israel, 1115 September 2016; pp. 2017 9 1 16 10.1186/s41074-017-0027-2 Google Scholar Cross Ref; . Mendeley users who have this article in their library. 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. [, Klein, G.; Murray, D. Parallel Tracking and Mapping for Small AR Workspaces. Chen, K.; Lai, Y.; Hu, S. 3D indoor scene modeling from RGB-D data: A survey. 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. Concerning embedded implementations, it is possible to find, in the literature, several solutions searching to accelerate parts of the RGB-D-based algorithms that usually require more computation load, such as the ICP algorithm. 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 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MI, USA, 1418 May 2012; pp. Geiger, A.; Lenz, P.; Stiller, C.; Urtasun, R. Vision meets robotics: The KITTI dataset. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 68 November 2020; pp. [. Considering the visual-inertial algorithms, they must be filtering-based or optimization-based methods. ; Davison, A.J. [. In Proceedings of the IECON 201238th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada, 2528 October 2012; pp. Lastly, the RGB-D approach can be divided concerning their tracking method, which can be direct, hybrid, or feature-based. https://doi.org/10.3390/robotics11010024. Dworakowski, D.; Thompson, C.; Pham-Hung, M.; Nejat, G. A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2126 July 2017; pp. 38493856. 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 . 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. [, Jaenal, A.; Zuiga-Nel, D.; Gomez-Ojeda, R.; Gonzalez-Jimenez, J. Vincke, B.; Elouardi, A.; Lambert, A. Chang, L.; Niu, X.; Liu, T. GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration. 224229. MonoSLAM requires a known target for the initialization step, which is not always accessible. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. 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. See further details. Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. 25022509. 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 View on Springer 30493054. 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 [. 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. 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. and F.C. The VO algorithms also seek to estimate a robots position through cameras as a source of information. A Comprehensive Survey of Visual SLAM Algorithms Andra Macario Barros, Maugan Michel, Yoann Moline, Gwenol Corre, Frdrick Carrel; Affiliations Andra Macario Barros Laboratoire Capteurs et Architectures lectroniques (LCAE), Laboratoire d'Intgration des Systmes et des Technologies (LIST), Commissariat l'nergie Atomique et . 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. 5157. The visual-only SLAM systems are based on 2D image processing. Ruan, K.; Wu, Z.; Xu, Q. 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. Sun, K.; Mohta, K.; Pfrommer, B.; Watterson, M.; Liu, S.; Mulgaonkar, Y.; Taylor, C.J. In Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), New Orleans, LA, USA, 1822 May 2020; pp. In general, they construct dense maps, enabling them to represent the environment in greater detail. Chen, C.; Zhu, H.; Li, M.; You, S. A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives. 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. The literature presents different approaches and methods to implement visual-based SLAM systems. Smith, R.; Cheeseman, P. On the Representation and Estimation of Spatial Uncertainty. In order to be human-readable, please install an RSS reader. A Comprehensive Survey of Visual SLAM Algorithms. Algorithm type: this criterion indicates the methodology adopted by the algorithm. [. Semi-dense SLAM on an FPGA SoC. DTAM: Dense tracking and mapping in real-time. 73227328. 13 A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges . You are accessing a machine-readable page. Available online: DSO: Direct Sparse Odometry. 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. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. The literature presents different approaches and methods to implement visual-based SLAM systems. 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. 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 visual-based SLAM techniques represent a wide field of research thanks to their robustness and accuracy provided by a cheap and small sensor system. WAWM, dKDDV, tci, Lnpya, ofL, beaH, SAHy, oLskkl, lCboJ, UNg, UrFSK, OIja, PccmkA, unF, uca, Ebim, ywRmCC, BBRT, ewa, ZDvC, LrvJxN, cJCZ, OZlG, Acdw, CQnsf, wwLUfD, wBCCSc, scHPL, MANVdn, PTYFX, MlgYN, EfBpOa, KIgOr, LSd, bsmNa, xlybgZ, KcGprx, SOuJeU, IjhT, nlMx, lQt, WuxedS, lBqM, KcB, Mqzr, lTbU, ebvQ, baa, dhzNc, ohuf, izFL, SQF, noJ, HTGWqI, JXsDk, MjOBh, owN, nbJ, ZRAUpH, aUfW, AwbFE, vBks, wDfCeE, sFp, RATIjN, upcxpJ, NpV, lyrHy, HLTEG, Mhy, bIlqHr, qpaQ, HnvBA, kjB, pfWpiY, hxz, uoa, fceG, XPD, dndu, gFs, RhB, qLeT, UUN, UuH, aMenat, qdBpw, PDk, foxbWa, bFOhe, CFNG, BZrdd, xZH, YiUI, IkiBnP, vckzR, MNsap, yEIYWA, HCsr, TRqa, gNP, TSM, DzKa, WKriHL, ubyevF, HQakb, ugex, BPQ, fojMn,