Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The Kalman filter: an introduction to concepts
Autonomous robot vehicles
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
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Automated Method for Large-Scale, Ground-Based City Model Acquisition
International Journal of Computer Vision
A multi-resolution pyramid for outdoor robot terrain perception
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Improving simultaneous mapping and localization in 3D using global constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Towards semantic maps for mobile robots
Robotics and Autonomous Systems
Acquiring a Physical World and Serving Its Mirror World Simultaneously
VMR '09 Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009
Autonomous driving in a multi-level parking structure
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic estimation of multi-level terrain maps
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
3D mapping with multi-resolution occupied voxel lists
Autonomous Robots
Space-carving Kernels for Accurate Rough Terrain Estimation
International Journal of Robotics Research
Three-dimensional iterative closest point-based outdoor SLAM using terrain classification
Intelligent Service Robotics
Probabilistic multi-level maps from LIDAR data
International Journal of Robotics Research
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Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2½-dimensional representation, is disadvantageous when utilized for mapping with mobile robots operating on the ground, since vertical or overhanging objects cannot be represented appropriately. Furthermore, such objects can lead to registration errors when two elevation maps have to be matched. In this paper, an approach is proposed that allows a mobile robot to deal with vertical and overhanging objects in elevation maps. The approach classifies the points in the environment according to whether they correspond to such objects or not. Also presented is a variant of the ICP algorithm that utilizes the classification of cells during the data association. Additionally, it is shown how the constraints computed by the ICP algorithm can be applied to determine globally consistent alignments. Experiments carried out with a real robot in an outdoor environment demonstrate that the proposed approach yields highly accurate elevation maps even in the case of loops. Experimental results are presented demonstrating that that the proposed classification increases the robustness of the scan matching process.