Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Ten lectures on wavelets
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Improved Occupancy Grids for Map Building
Autonomous Robots
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Active Appearance-Based Robot Localization Using Stereo Vision
Autonomous Robots
A Supervised Learning Framework for Generic Object Detection in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The revisiting problem in mobile robot map building: a hierarchical bayesian approach
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
Online and Incremental Appearance-based SLAM in Highly Dynamic Environments
International Journal of Robotics Research
Loop-closing: A typicality approach
Robotics and Autonomous Systems
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In this paper, an appearance-based environment modeling technique is presented. Based on this approach, the probabilistic Bayesian inference can work together with a symbolic topological map to relocalize a mobile robot. One prominent advantage offered by this algorithm is that it can be applied to a cross-country environment where no features or landmarks are available. Further more, the loop-closing can be detected independently of estimated map and vehicle location. High dimensional laser measurements are projected into a low dimensional space (mapspace) which describes the appearance of the environment. Since laser scans from the same region share a similar appearance, after the projection, they are expected to form a distinct cluster in the low dimensional space. This small cluster essentially encodes appearance information of the specific region in the environment, and it can be approximated by a Gaussian distribution. This Gaussian model can serve as the "joint" between the topological map structure and the probabilistic Bayesian inference. By employing such "joints", the Bayesian inference in the metric level can be conveniently implemented on a topological level. Based on appearance, the proposed inference process is thus completely independent of local metric features. Extensive experiments were conducted using a tracked vehicle traveling in an open jungle environment. Results from live runs verified the feasibility of using the proposed methods to detect loop-closing. The performances are also given and thoroughly analyzed.