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AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning to explore and build maps
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Distributed Multi-Robot Exploration and Mapping
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
A mixture-model based algorithm for real-time terrain estimation: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing
International Journal of Robotics Research
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Effective maximum likelihood grid map withconflict evaluation filter using sonar sensors
IEEE Transactions on Robotics
Contextual occupancy maps using Gaussian processes
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
Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Coordinated multi-robot exploration
IEEE Transactions on Robotics
International Journal of Robotics Research
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Recent research has shown that robots can model their world with Multi-Level (ML) maps, which utilize patches in a two-dimensional grid space to represent various environment elevations within a given grid cell. Although these maps are able to produce three-dimensional models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into patches. To respond to these drawbacks, this paper proposes to extend these ML maps into Probabilistic Multi-Level (PML) maps, which use formal probability theory to incorporate estimation and modeling errors due to uncertainty. Measurements are probabilistically associated with cells near the nominal location, and are categorized through hypothesis testing into patches via classification methods that incorporate uncertainty. Experimental results on representative objects found in both indoor and outdoor environments show that PML generally outperforms ML, including in noisy and sparse data environments, by producing more consistent, informative and conservative maps. In addition, PML provides the framework to heterogeneous, cooperative mapping and a way to probabilistically discriminate between conflicting maps.