Streaming Loop-Closure Selection under Memory Constraints in Graph-SLAM

Abstract

Graph-based SLAM models robot poses as vertices and relative-pose measurements (odometry and loop-closures) as edges. Odometry edges are always kept to preserve connectivity, while loop-closure edges reduce drift but cannot all be stored due to memory or computation limits. Our challenge is to decide online which closures to keep under a strict budget, when the full set of measurements cannot be stored or centralized. Prior work instead addresses an offline problem that assumes access to the complete pose graph and optimizes a log-determinant (D-optimality) surrogate. In the online regime, an additional difficulty arises because the odometry backbone grows over time and the utility of each loop-closure changes as the graph evolves. We formulate this problem as streaming submodular maximization with a time-varying log-determinant objective. We propose a one-pass preemptive greedy policy that operates with exactly k memory slots for loop-closures. We show that, under arbitrary arrival order, it achieves a uniform constant-factor guarantee on the log-determinant improvement beyond an odometry-only baseline, relative to the hindsight-optimal size-k solution. On benchmark data, the proposed method closely matches offline greedy despite the conservative bound, showing that principled streaming selection can recover most of the benefit of loop-closures while respecting resource limits.