Motion Detection Development for Dynamic SLAM Based on Epipolar Geometry

Dynamic environments pose a major challenge for visual SLAM, as independently moving objects introduce feature correspondences that violate the static-scene assumption and degrade pose estimation. To address this, we propose a geometry-based filtering method that augments classical epipolar residuals with a new Epipolar Direction Consistency (EDC) metric. For each feature match, EDC evaluates the angular agreement between the observed optical-flow vector and the tangent direction of its corresponding epipolar line. This directional cue, combined with positional residuals in an adaptive scoring scheme and refined through short-window temporal voting, enables reliable separation of static inliers from dynamic outliers without requiring learning-based models or semantic information. The method is lightweight, easily integrated into feature-based SLAM pipelines, and automatically adapts to varying motion levels using MAD-based thresholds. Experiments demonstrate that inserting the EDC filter into a standard ORB-style pipeline improves trajectory stability and accuracy by reducing drift caused by moving objects, while preserving real-time performance. Overall, EDC provides a simple, interpretable, and training-free mechanism for enhancing SLAM robustness in dynamic scenes.