A visual SLAM algorithm based on illumination-robust feature extraction and dynamic feature removal

doi:10.13474/j.cnki.11-2246.2025.0917

Abstract

Abstract: To address the issue of low localization accuracy in visual SLAM algorithms caused by environmental moving objects and illumination changes,this paper proposes a high-precision visual SLAM algorithm suitable for dynamic and varying illumination environments.This algorithm is based on the VINS Mono architecture,firstly,it performs illumination-robust feature extraction by extracting features from input images through a ResNet network to obtain initial feature point coordinates and illumination-invariant feature maps; then,it performs optical flow estimation on these maps to reduce the impact of illumination on feature tracking.Subsequently,it carries out dynamic feature removal by using pixel-level semantic segmentation with YOLOv8 to mark dynamic objects in the input images as masks.Epipolar geometry constraints are then utilized to remove dynamic features within the mask regions,obtaining stable static feature points for tracking and reducing the impact of dynamic features on the algorithm's localization accuracy.Finally,comparative experiments on the EuRoC,VIODE,and Market datasets show that our method achieves 55.09%lower absolute trajectory error compared to VINS-Mono,demonstrating good localization accuracy in dynamic and varying illumination environments.

Key words: dynamic feature removal, illumination-robust feature extraction, optical flow method, VINS Mono

CLC Number:

P228

KE Xueliang, XIAO Wei, QU Naizhu, HE Zhijie, HUANG Rui. A visual SLAM algorithm based on illumination-robust feature extraction and dynamic feature removal[J]. Bulletin of Surveying and Mapping, 2025, 0(9): 105-111.

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