Adaptive Outlier Thresholding for Bundle Adjustment in Visual SLAM

Abstract

State-of-the-art V-SLAM pipelines utilize robust cost functions and outlier rejection techniques to remove incorrect correspondences. However, these methods are typically fine-tuned to overfit certain benchmarks and struggle to adapt effectively to changes in the application domain or environmental conditions. This renders them impractical for many robotic applications in which robustness in a wide variety of conditions is essential. In this paper we introduce a novel distribution-based approach for online outlier rejection that reduces the necessity for scene-specific fine-tuning while simultaneously improving the overall SLAM performance. Through experiments across 3 different public datasets, we show that our approach consistently outperforms state-of-the-art methods in various real-world settings. Our code is available at https://github.com/alejandrofontan/ORB_SLAM2_Distribution