Abstract: Active map-to-frame matching using SLAM condition scoring is proposed for balancing time cost and accuracy in indirect, BA-SLAM. Exploiting the submodularity property of pose optimization condition scoring leads to an algorithm for deciding when to employ map-to-frame matching, how many points to select, and how to prioritize the data association to best benefit pose estimation outcomes. When applied to ORB-SLAM, the algorithmic modification lowers run-time costs without impacting localization performance.

Abstract: Long-term, large-area SLAM performance is best when it is possible to match previously seen (but subsequently lost) map points when they re-enter the field-of-view and are visible once more. However, the time cost associated to matching against an ever increasing map undermines real-time performance. We combine hashing and condition scoring to arrive at a fast, bounded map-to-frame method we call Map-Hash. The value of Map-Hash is shown on a modified ORB-SLAM implementation relative to baseline ORB-SLAM.