PRE-SLAM: Persistence Reasoning in Edge-assisted Visual SLAM

Visual-SLAM systems involve computationally in-tense operations and are challenging to run on embedded devices. One method to alleviate resource constraints is to leverage the edge computing paradigm to offload computationally heavy tasks. Limiting the resource use of Visual-SLAM on a mobile device allows us to deploy such systems on diverse hardware including wearables as well as enable long-term operation. Long-term operation brings other challenges however, such as the need to observe changes in the scene on repeated visits. To address semi-static scenes, there has been some recent work in designing techniques that can filter out these dynamic observations [1] called feature persistence filtering. Recently, such filtering has been demonstrated using Visual-SLAM systems as well [2]. In this work, we introduce PRE-SLAM, which builds upon the edge-assisted Visual-SLAM system, Edge-SLAM [3], to incorporate feature persistence filtering. We revisit the centralized persistence filter architecture and make a series of modifications to allow for dynamic feature filtering in an edge-assisted setting. Using two locally collected datasets, we show how our split persistent filter implementation is comparable with the centralized version in performance, reducing map-point and keyframe retention by 26.6 % and 16.6 % respectively. By filtering out dynamic map-points from the system, we demonstrate an improvement in average localization accuracy by more than 50%. We also demonstrate how incorporating feature persistence filtering into Edge-SLAM retains the key benefits and performance enhance-ments of an edge-assisted Visual-SLAM system, with an added communication overhead of only 500 KB while decreasing overall man size by 8.6 %.