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FLoTS-SLAM: Two-Stage Optimization Using Optical Flow Uncertainty for Dynamic Monocular SLAM

FLoTS-SLAM proposes deriving motion uncertainty from the residual between optical flow and camera-motion-induced flow, for monocular RGB SLAM in dynamic scenes. The system first estimates camera pose using high-confidence pixels, then reprojects dynamic pixels to jointly optimize pose and depth, reducing the impact of object motion on pose and depth updates. On DAVIS, the average IoU of dynamic masks improves by 35% over advanced baselines; on Bonn and TUM, average ATE is reduced by 59% and 19%, respectively, relative to the SLAM backbone used. The source code will be made public.

Part of the event: FLoTS-SLAM Uses Optical Flow Uncertainty to Improve Dynamic SLAM Accuracy

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