LSH-RANSAC: an incremental scheme for scalable localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Autonomous driving in a multi-level parking structure
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
3D mapping with multi-resolution occupied voxel lists
Autonomous Robots
Active-semantic localization with a single consumer-grade camera
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Vehicle 3D localization in mountainous woodland environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An efficient area-based observation model for Monte-Carlo robot localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
Local map-based exploration for mobile robots
Intelligent Service Robotics
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We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filter to estimate the full six-dimensional state of the robot and utilizes multilevel surface maps, which, in contrast to standard elevation maps, allow the robot to represent vertical structures and multiple levels in the environment. We describe probabilistic motion and sensor models to calculate the proposal distribution and to evaluate the likelihood of observations. We furthermore describe an active localization approach that actively selects the sensor orientation of the two-dimensional laser range scanner to improve the localization results. To efficiently calculate the appropriate orientation, we apply a clustering operation on the particles and evaluate potential orientations on the basis of these clusters. Experimental results obtained with a mobile robot in large-scale outdoor environments indicate that our approach yields robust and accurate position estimates. The experiments also demonstrate that multilevel surface maps lead to a significantly better localization performance than standard elevation maps. They additionally show that further accuracy is obtained from the active sensing approach. © 2008 Wiley Periodicals, Inc.