Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Communications of the ACM - Robots: intelligence, versatility, adaptivity
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Mathematical Foundations of Navigation and Perception for an Autonomous Mobile Robot
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Monte Carlo Filter in Mobile Robotics Localization: A Clustered Evolutionary Point of View
Journal of Intelligent and Robotic Systems
A relative map approach to SLAM based on shift and rotation invariants
Robotics and Autonomous Systems
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Robust outdoor visual localization using a three-dimensional-edge map
Journal of Field Robotics
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Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.