Semantic Masking and Visual Feature Matching for Robust Localization

TL;DR

This paper introduces a semantic masking technique that enhances the robustness and accuracy of visual localization for autonomous robots in dynamic, unstructured environments like the International Space Station, especially under limited computational resources.

Contribution

The paper presents a lightweight semantic masking method that enforces static object matches, improving long-term visual localization accuracy in changing environments, suitable for low-compute space robots.

Findings

Improved Absolute Trajectory Error (ATE) in experiments.

Higher correct match ratios on the Astrobee dataset.

Enhanced robustness of visual localization in dynamic settings.

Abstract

We are interested in long-term deployments of autonomous robots to aid astronauts with maintenance and monitoring operations in settings such as the International Space Station. Unfortunately, such environments tend to be highly dynamic and unstructured, and their frequent reconfiguration poses a challenge for robust long-term localization of robots. Many state-of-the-art visual feature-based localization algorithms are not robust towards spatial scene changes, and SLAM algorithms, while promising, cannot run within the low-compute budget available to space robots. To address this gap, we present a computationally efficient semantic masking approach for visual feature matching that improves the accuracy and robustness of visual localization systems during long-term deployment in changing environments. Our method introduces a lightweight check that enforces matches to be within long-term static objects and have consistent semantic classes. We evaluate this approach using both map-based relocalization and relative pose estimation and show that it improves Absolute Trajectory Error (ATE) and correct match ratios on the publicly available Astrobee dataset. While this approach was originally developed for microgravity robotic freeflyers, it can be applied to any visual feature matching pipeline to improve robustness.