Robust simultaneous localization and mapping in low‐light environment

Abstract

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.

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

“…To this end, a more robust performance in environments with challenging illumination conditions was achieved. Huang et al [22] processed ORB and Brisk feature points at the same time for a low-lighting environment to improve the robustness of the VSLAM system. However, extracting multiple features needs extra computing resources.…”

Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network

“…The reconstruction of semantically coherent scene was obtained from a single perspective. Huang and Liu [17] proposed a novel multiexposure image enhancement method, which can generate a multi-exposure image sequence. Rublee et al [18] proposed a multi-feature extraction algorithm to extract two kinds of image features simultaneously, which can work effectively if the single feature algorithm fails to extract enough feature points in SLAM.…”

Benchmarking visual SLAM methods in mirror environments

“…Estimation of camera pose from images can be hindered by a range of environmental circumstances [8,10]. Many visual complexities have been considered in the literature, and methods proposed to overcome specific difficulties: motion blur [11][12][13], illumination change [14,15], dynamic scenes [16,17], textures [18][19][20], indoor/outdoor transitions, and specular highlights [18,21]. General approaches to tackle complexities have recently been proposed [18,22], but do so indiscriminately of the source of errors.…”