AFE-ORB-SLAM: Robust Monocular VSLAM Based on Adaptive FAST Threshold and Image Enhancement for Complex Lighting Environments

Abstract

Monocular Visual Simultaneous Localisation and Mapping (VSLAM) systems are widely utilised for intelligent mobile robots to work in unknown environments. However, complex and varying illuminations challenge the accuracy and robustness of VSLAM systems significantly. Existing feature-based VSLAM methods often fail due to the insufficient feature points that can be extracted in those challenging illumination environments. Therefore, this paper proposes an improved ORB-SLAM algorithm based on adaptive FAST threshold and image enhancement (AFE-ORB-SLAM), which works in the environments with complex lighting conditions. An improved truncated Adaptive Gamma Correction (AGC) is combined with unsharp masking to reduce the effect caused by different illuminations. What is more, an improved ORB feature extraction method with the adaptive FAST threshold is proposed and adopted to obtain more reliable feature points. To verify the performance of the AFE-ORB-SLAM, three public datasets (the extended Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset with different lighting conditions, Onboard Illumination Visual-Inertial Odometry (OIVIO) dataset and the European Robotics Challenge (EuRoC) dataset) are utilised. The results are compared with other state-of-the-art monocular VSLAM methods. The experimental results demonstrate that the AFE-ORB-SLAM could achieve the highest average localisation accuracy with robust performance in the environments with complex lighting conditions while keeping similar performance in the normal lighting scenarios.