Enhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter

2025-02-03

AI Summary

In GPS-denied visual navigation, unreliable feature tracking—due to occlusion or rapid motion—often leads to pose estimation degradation. To address this, this work introduces, for the first time, a kinematic invariance set formulation for feature visibility constraints and designs a real-time safety filter based on quadratic programming (QP) that minimizes deviation while enforcing persistent observability of a sufficient number of features. The method unifies robot kinematics, visual SLAM, and information-entropy-driven visibility metrics to jointly optimize perceptual robustness and control accuracy. Simulation results verify constraint invariance, while real-world SLAM experiments demonstrate significant improvements in pose estimation accuracy under challenging conditions—including texture-poor environments and high-speed motion—outperforming existing baseline controllers.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, localization is achieved by detecting and tracking visual features or landmarks, which provide information about the sensor's relative pose. For reliable feature tracking and accurate pose estimation, it is crucial to maintain visibility of a sufficient number of features. This requirement can sometimes conflict with the robot's overall task objective. In this paper, we approach it as a constrained control problem. By leveraging the invariance properties of visibility constraints within the robot's kinematic model, we propose a real-time safety filter based on quadratic programming. This filter takes a reference velocity command as input and produces a modified velocity that minimally deviates from the reference while ensuring the information score from the currently visible features remains above a user-specified threshold. Numerical simulations demonstrate that the proposed safety filter preserves the invariance condition and ensures the visibility of more features than the required minimum. We also validated its real-world performance by integrating it into a visual simultaneous localization and mapping (SLAM) algorithm, where it maintained high estimation quality in challenging environments, outperforming a simple tracking controller.

Problem

• Visual Navigation
• Object Tracking
• Robot Localization

Innovation

• Real-time Filtering
• Visual Navigation
• Object Tracking