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

2025-09-26

AI Summary

Visual SLAM often suffers from tracking failure and inaccurate pose estimation in visually degraded scenarios—such as textureless environments or low-light conditions—due to its reliance on appearance-based features. To address this, we propose a tightly coupled framework that adaptively fuses dead reckoning (DR) with visual SLAM. Our approach innovatively restructures both the frontend and backend: DR measurements from IMU and wheel encoders are deeply integrated into feature tracking and pose optimization. Furthermore, we introduce a confidence-aware dynamic weighting mechanism that adjusts the visual–DR fusion ratio at the frame level, automatically increasing DR contribution under visual degradation. Extensive evaluations on public benchmarks and real-world scenes demonstrate significant improvements in trajectory continuity and absolute pose accuracy. Notably, our method exhibits superior robustness over state-of-the-art visual SLAM systems in challenging conditions—including uniform-color walls and weak illumination.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 Weights (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 Weights 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 Weights.

Problem

• Fusing dead reckoning with visual SLAM for robust pose estimation
• Adaptively balancing sensor inputs during visual tracking failures
• Maintaining continuous localization in texture-less or degraded environments

Innovation

• Adaptively fuses dead reckoning with visual SLAM
• Modifies SLAM modules to incorporate dead reckoning
• Uses adaptive weighting based on visual reliability