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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 article introduces an improved ORB-SLAM3 algorithm based on image enhancement for constrained hardware resources adaptive-light enhanced visual simultaneous localization and mapping (ALE-vSLAM), aimed at addressing challenges in varying lighting conditions. First, we have released a proprietary data set 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. Second, to more accurately extract feature points across various scales, we propose an adaptive contrast-guided contrast limited adaptive histogram equalization (CLAHE) enhancement method, in conjunction with an image pyramid. Finally, we have developed a CLAHE-integrated features from the accelerated segment test corner detection strategy that reincorporates important feature points filtered out due to lighting changes, while mitigating noise impact as much as possible. The experiments utilized the European robotics challenge data set and our own data set (SLAMLightClass data set). Results indicate that under conditions of limited resources, ALE-vSLAM consistently outperforms the ORB-SLAM3 algorithm in both positioning accuracy and the robustness of the feature extraction.
Hu et al. (Wed,) studied this question.