Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM

Abstract-- Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., plain walls or low lighting) lead to poor pose estimation and track loss. However, robots are typically equipped with sensors that provide some form of dead reckoning odometry with reasonable short-time performance but unreliable long-time performance. The Good W eights (GW) algorithm described here provides a framework to adaptively integrate dead reckoning (DR) with passive visual SLAM for continuous and accurate frame-level pose estimation. Importantly, it describes how all modules in a comprehensive SLAM system must be modified to incorporate DR into its design. Adaptive weighting increases DR influence when visual tracking is unreliable and reduces when visual feature information is strong, maintaining pose track without overreliance on DR. Good W eights yields a practical solution for mobile navigation that improves visual SLAM performance and robustness. Experiments on collected datasets and in real-world deployment demonstrate the benefits of Good W eights. Keywords: Visual SLAM, dead reckoning, feature tracking, optimization Visual Simultaneous Localization and Mapping (SLAM) is often formulated as a nonlinear least-squares problem, where camera poses and 3D landmarks are jointly estimated from visual observations [1]-[3]. Optimization accuracy and stability depends on the sufficiency and reliability of feature associations across frames, short-term and long-term.