Conference Proceeding
Uncertain map making in natural environments
Lab. d'Autom. et d'Anal. des Syst., CNRS, Toulouse
05/1996;
DOI:10.1109/ROBOT.1996.506847
ISBN: 0-7803-2988-0 pp.1048 - 1053 vol.2 In proceeding of: Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on, Volume: 2
Source: IEEE Xplore
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Citations (0)
- Cited In (4)
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Conference Proceeding: Autonomous Terrain Mapping and Classification Using Hidden Markov Models
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ABSTRACT: This paper presents a new approach for terrain mapping and classification using mobile robots with 2D laser range finders. Our algorithm generates 3D terrain maps and classifies navigable and non-navigable regions on those maps using Hidden Markov models. The maps generated by our approach can be used for path planning, navigation, local obstacle avoidance, detection of changes in the terrain, and object recognition. We propose a map segmentation algorithm based on Markov Random Fields, which removes small errors in the classification. In order to validate our algorithms, we present experimental results using two robotic platforms.Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on; 05/2005 -
Conference Proceeding: Visually built task models for robot teams in unstructured environments
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ABSTRACT: In field environments it is not usually possible to provide robotic systems with valid geometric models of the task and environment. The robot or robot teams will need to create these models by performing appropriate sensor actions. Here, an algorithm based on iterative sensor planning and sensor redundancy is proposed to enable them to efficiently build 3D models of the environment and task. The method assumes stationary robotic vehicles with cameras carried by articulated mounts. The algorithm uses the measured scene information to find new camera mount poses based on information content. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulations show the effectiveness of this algorithm.Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on; 02/2002 -
Article: Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs.
Journal of Intelligent and Robotic Systems. 01/2009; 54:137-161.
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Keywords
anchoring
extended Kalman filter
incremental natural scene modelling
Landmarks
meters
mobile robot navigation
Model updating
Objects
robot localization
specific properties
uncertain state vector