Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
How are the locations of objects in the environment represented in memory?
Spatial cognition III
Spatial knowledge representation for human-robot interaction
Spatial cognition III
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Spatial language for human-robot dialogs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
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This paper presents a very efficient SLAM algorithm that works by hierarchically dividing the map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this region. For keeping the matrices small only those landmarks are represented being observable from outside the region. A measurement is integrated into a local subregion using O(k2) computation time for k landmarks in a subregion. When the robot moves to a different subregion a global update is necessary requiring only O(k3 log n) computation time for n overall landmarks. The algorithm is evaluated for map quality, storage space and computation time using simulated and real experiments in an office environment.