Improving the Performance of Local Bundle Adjustment for Visual-Inertial SLAM with Efficient Use of GPU Resources

In this paper, we present our approach to efficiently leveraging GPU resources to improve the performance of local bundle adjustment for visual-inertial SLAM. We observe that for local bundle adjustment (i) the Schur complement method, a technique often used to speed up bundle adjustment, has the largest overhead when solving for the parameter update, and (ii) the workload consists of operations on small- to medium-sized matrices. Based on these observations, we develop and combine several techniques that efficiently handle small- to medium-sized matrices. We then implement these techniques as a drop-in replacement block solver for g2o, a library frequently used for bundle adjustment, and integrate it with ORB-SLAM3, a well-known open-source visual-inertial SLAM system. Our evaluation done with two popular datasets, EuRoC and TUM-VI, shows that we can reduce the time taken by local bundle adjustment by 13.81%-33.79% with our techniques across an embedded device and a desktop machine.