IRAF-SLAM: An Illumination-Robust and Adaptive Feature-Culling Front-End for Visual SLAM in Challenging Environments
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Robust Visual SLAM (vSLAM) is essential for autonomous systems operating in
real-world environments, where challenges such as dynamic objects, low texture,
and critically, varying illumination conditions often degrade performance.
Existing feature-based SLAM systems rely on fixed front-end parameters, making
them vulnerable to sudden lighting changes and unstable feature tracking. To
address these challenges, we propose ``IRAF-SLAM'', an Illumination-Robust and
Adaptive Feature-Culling front-end designed to enhance vSLAM resilience in
complex and challenging environments. Our approach introduces: (1) an image
enhancement scheme to preprocess and adjust image quality under varying
lighting conditions; (2) an adaptive feature extraction mechanism that
dynamically adjusts detection sensitivity based on image entropy, pixel
intensity, and gradient analysis; and (3) a feature culling strategy that
filters out unreliable feature points using density distribution analysis and a
lighting impact factor. Comprehensive evaluations on the TUM-VI and European
Robotics Challenge (EuRoC) datasets demonstrate that IRAF-SLAM significantly
reduces tracking failures and achieves superior trajectory accuracy compared to
state-of-the-art vSLAM methods under adverse illumination conditions. These
results highlight the effectiveness of adaptive front-end strategies in
improving vSLAM robustness without incurring significant computational
overhead. The implementation of IRAF-SLAM is publicly available at
https://thanhnguyencanh. github.io/IRAF-SLAM/.
