Efficient information-theoretic graph pruning for graph-based SLAM with laser range finders

In graph-based SLAM, the pose graph encodes the poses of the robot during data acquisition as well as spatial constraints between them. The size of the pose graph has a substantial influence on the runtime and the memory requirements of a SLAM system, which hinders long-term mapping. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the expected information gain of laser measurements with respect to the resulting occupancy grid map. It allows for restricting the size of the pose graph depending on the information that the robot acquires about the environment or based on a given memory limit, which results in an any-space SLAM system. When discarding laser scans, our approach marginalizes out the corresponding pose nodes from the graph. To avoid a densely connected pose graph, which would result from exact marginalization, we propose an approximation to marginalization that is based on local Chow-Liu trees and maintains a sparse graph. Real world experiments suggest that our approach effectively reduces the growth of the pose graph while minimizing the loss of information in the resulting grid map.

Authors

• Henrik Kretzschmar
• Cyrill Stachniss
• Giorgio Grisetti

Keywords

• Measurement by laser beam
• Simultaneous localization and mapping
• Approximation methods
• Lasers
• Mutual information
• Laser beams
• Laser Ranging
• Graph Pruning
• Laser Scanning
• Limited Memory
• Grid Map
• Graph Size
• Sparse Graph
• Robot Pose
• Laser Measurement
• Sparsity
• Laser Beam
• Grid Cells
• Kullback-Leibler
• Nodes In The Graph
• Number Of Scans
• Fisher Information
• Optimal Estimation
• Markov Random Field
• Set Of Scans
• Gaussian Markov Random Field
• Loop Closure
• Current Scan
• Pruning Techniques
• Graph Optimization
• Schur Complement
• Conditional Entropy
• Odometry
• Environment Map