On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Active Search for Real-Time Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Robust and efficient robotic mapping
Robust and efficient robotic mapping
Efficient optimization of information-theoretic exploration in SLAM
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Covariance recovery from a square root information matrix for data association
Robotics and Autonomous Systems
Real-time correlative scan matching
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Automatically and efficiently inferring the hierarchical structure of visual maps
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Information-based compact pose SLAM
IEEE Transactions on Robotics
RANGE–Robust autonomous navigation in GPS-denied environments
Journal of Field Robotics
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Lifelong localization in changing environments
International Journal of Robotics Research
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In graph-based simultaneous localization and mapping (SLAM), the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow-Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph. Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping.