Partial Hierarchical Pose Graph Optimization for SLAM
This addresses efficiency issues in robotics and autonomous systems, though it appears incremental as it builds on existing hierarchical optimization approaches.
The paper tackles the computational bottleneck in SLAM by proposing a partial hierarchical pose graph optimization method, achieving a 10x speedup compared to original optimization without sacrificing quality.
In this paper we consider a hierarchical pose graph optimization (HPGO) for Simultaneous Localization and Mapping (SLAM). We propose a fast incremental procedure for building hierarchy levels in pose graphs. We study the properties of this procedure and show that our solution delivers high execution speed, high reduction rate and good flexibility. We propose a way to do partial hierarchical optimization and compare it to other optimization modes. We show that given a comparatively large amount of poses, partial HPGO gives a 10x speed up comparing to the original optimization, not sacrificing the quality.