A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
3-D Modelling and Robot Localization from Visual and Range Data in Natural Scenes
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing
International Journal of Robotics Research
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
Improving simultaneous mapping and localization in 3D using global constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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To navigate in an unknown environment, a robot should build a model for the environment. For outdoor environments, an elevation map is used as the main world model. We considered the outdoor simultaneous localization and mapping (SLAM) method to build a global elevation map by matching local elevation maps. In this research, the iterative closest point (ICP) algorithm was used to match local elevation maps and estimate a robot pose. However, an alignment error is generated by the ICP algorithm due to false selection of corresponding points. Therefore, we propose a new method to classify environmental data into several groups, and to find the corresponding points correctly and improve the performance of the ICP algorithm. Different weights are assigned according to the classified groups because certain groups are very sensitive to the viewpoint of the robot. Three-dimensional (3-D) environmental data acquired by tilting a 2-D laser scanner are used to build local elevation maps and to classify each grid of the map. Experimental results in real environments show the increased accuracy of the proposed ICP-based matching and a reduction in matching time.