An Adaptive Lighting Indoor vSLAM With Limited On-Device Resources

Visual Simultaneous Localization and Mapping (vSLAM) is a critical technology for enabling robots to achieve self-awareness in unknown environments. However, the robustness and precision of vSLAM are continuously challenged by issues such as sudden changes in lighting, reflections from shadows, and reduced contrast within indoor settings under constrained hardware resources. This paper introduces an improved ORB-SLAM3 algorithm based on image enhancement for constrained hardware resources (ALE-vSLAM, Adaptive-Light Enhanced Visual SLAM), aimed at addressing challenges in varying lighting conditions. Firstly, we have released a proprietary dataset specifically targeting issues related to lighting variations, combined with image enhancement techniques to categorize and process the textural and structural features of images under different lighting conditions. Secondly, to more accurately extract feature points across various scales, we propose an adaptive contrast-guided CLAHE enhancement method, in conjunction with an image pyramid. Lastly, we have developed a CLAHE-integrated FAST corner detection strategy that re-incorporates important feature points filtered out due to lighting changes, while mitigating noise impact as much as possible. The experiments utilized the EuRoC dataset and our own dataset (SLAMLightClass Dataset). Results indicate that under conditions of limited resources, ALE-vSLAM consistently outperforms the ORB-SLAM3 algorithm in both positioning accuracy and the robustness of feature extraction.