Robust simultaneous localization and mapping in low‐light environment

Complex and varied illumination makes computer vision research studies difficult. This research field pays much attention to scenes with weak illumination, especially in visual simultaneous localization and mapping (SLAM). Although the current feature-based algorithm is mature, the existing SLAM method often fails because it cannot extract enough feature information in the low-light environment. In this paper, we propose a new solution to this problem, which allows our system to work in environments with the majority of lighting. We propose a multifeature extraction algorithm to extract two kinds of image features simultaneously. With such a solution, our system can work when the single-feature algorithm fails to extract enough feature points. We also add an image preprocessing step before tracking thread to cope with extremely dark conditions. Finally, we fully evaluate our approach on existing public data sets. Experiments show that the method combining multiple features can improve the robustness of the state-of-the-art algorithm under weak illumination without affecting the real-time performance.