Comparing the usefulness of video and map information in navigation tasks
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
LASSOing HRI: analyzing situation awareness in map-centric and video-centric interfaces
Proceedings of the ACM/IEEE international conference on Human-robot interaction
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
Multi-objective exploration and search for autonomous rescue robots: Research Articles
Journal of Field Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
Incremental feature-based mapping from sonar data using Gaussian mixture models
Proceedings of the 2011 ACM Symposium on Applied Computing
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This paper presents a feature based 3D mapping approach with regard to obtaining compact models of semistructured environments such as partially destroyed buildings where mobile robots are to carry out rescue activities. To gather the 3D data, we use a laser scanner, employing a nodding data acquisition system mounted on both real and simulated robots. Our segmentation algorithm comes up from the integration of computer vision techniques, allowing for a fast separation of points corresponding to different, not necessarily planar, surfaces. The subsequent extraction of geometrical features out of each region's points is done by means of least-squares fitting. A Maximum Incremental Probability algorithm formulated upon the Extended Kalman Filter provides 6D localization and produces a map of planar patches with a convex-hull based representation. Scenarios from the Unified System for Automation and Robot Simulation (USARSim), including world models from past RoboCup Rescue editions' arenas, have been utilized to conduct some of our experiments