Sensor fusion in certainty grids for mobile robots
Sensor devices and systems for robotics
Monte Carlo localization: efficient position estimation for mobile robots
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The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
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International Journal of Robotics Research
An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing
International Journal of Robotics Research
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach
Robotics and Autonomous Systems
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Heuristic-based laser scan matching for outdoor 6d SLAM
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Particle filters for positioning, navigation, and tracking
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Coordinated multi-robot exploration
IEEE Transactions on Robotics
Path planning in complex 3D environments using a probabilistic roadmap method
International Journal of Automation and Computing
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Most current navigation algorithms in mobile robotics produce 2D maps from data provided by 2D sensors. In large part this is due to the availability of suitable 3D sensors and difficulties of managing the large amount of data supplied by 3D sensors. This paper presents a novel, multi-resolution algorithm that aligns 3D range data stored in occupied voxel lists so as to facilitate the construction of 3D maps. Multi-resolution occupied voxel lists (MROL) are voxel based data structures that efficiently represent 3D scan and map information. The process described in this research can align a sequence of scans to produce maps and localise a range sensor within a prior global map. An office environment (200 square metres) is mapped in 3D at 0.02 m resolution, resulting in a 200,000 voxel occupied voxel list. Global localisation within this map, with no prior pose estimate, is completed in 5 seconds on a 2 GHz processor. The MROL based sequential scan matching is compared to a standard iterative closest point (ICP) implementation with an error in the initial pose estimate of plus or minus 1 m and 90 degrees. MROL correctly scan matches 94% of scans to within 0.1 m as opposed to ICP with 30% within 0.1 m.