Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Trajectory Optimization using Reinforcement Learning for Map Exploration
International Journal of Robotics Research
Control and self-localization of an omni-directional mobile robot
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
A practical approach to control and self-localization of an omni-directional mobile robot
WSEAS Transactions on Systems and Control
Probabilistic multi-component extended strong tracking filter for mobile robot global localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Scalable and convergent multi-robot passive and active sensing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
International Journal of Robotics Research
Experimental investigation of a prediction algorithm for an indoor SLAM platform
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots
Journal of Intelligent and Robotic Systems
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This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem in the stochastic map framework based on the concept of the relative map. The idea consists in introducing a map state, which only contains relative quantities among the features invariant under shift and rotation. The estimation of this relative state is carried out through an Extended Kalman Filter. The shift and rotation invariance of the state allows us to significantly reduce the computational burden. In particular, the computational requirement is independent of the number of features. Furthermore, since the estimation process is local, it is not affected by the linearization introduced by the EKF. The cases of point features and corner features are considered. Furthermore, in the case of corners, it is considered a realistic case of an indoor environment containing structures consisting of several corners. Finally, since a relative map contains dependent elements, the information coming from all the constraints which express the elements dependency, is exploited. For this, an approximated solution with low computational requirement is proposed. Its limitation arises at the loop closure since it cannot exploit the information in this case. This is discussed in depth for the case of point features. Experimental results carried out on a real platform in our laboratory and by using the Victoria park dataset show the performance of the approach.