Building, registrating, and fusing noisy visual maps
International Journal of Robotics Research - Special Issue on Sensor Data Fusion
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Shape registration using optimization for mobile robot navigation
Shape registration using optimization for mobile robot navigation
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
Visually Realistic Mapping of a Planar Environment with Stereo
ISER '00 Experimental Robotics VII
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Bearings-only localization and mapping
Bearings-only localization and mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust 3D SLAM with a stereo camera based on an edge-point ICP algorithm
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Scan matching SLAM in underwater environments
Autonomous Robots
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This paper gives a review of the literature on Simultaneous Localization and Mapping (SLAM). SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, particle filter). In this paper, we classify SLAM approaches into three main categories: visual SLAM, Lidar SLAM and sensor fusion SLAM, while visual and lidar can also contain many types and levels, such as monocular camera, stereovision, laser scanner, radar and fusion of these sensors. A number of promising approaches and recent developments in this literature have been reviewed in this paper. To give a better understanding of performance difference, an implementation of Lidar SLAM is presented with comparative analysis result.