ABSTRACT In existing research on SLAM systems, the corner point detection problem of the vision front‐end is usually abstracted as a feature recognition problem. However, traditional corner point detection algorithms are too sensitive to noise and susceptible to scale variations and luminance fluctuations, failing to fully and effectively capture the image information obtained from the vision front‐end sensors. To address this challenge, this paper proposes a new vision front‐end based ORB‐SLAM3 method, hereinafter referred to as BLO‐SLAM. The following three main innovations are proposed: (1) An optimized system model incorporating the BM3D denoising strategy, which significantly enhances feature extraction efficiency and improves edge feature‐point matching in low‐texture and low‐light environments; (2) To tackle the challenge of accurately capturing local pixel motions in scenarios involving rapid or minimal motion, an improved Lucas‐Kanade (LK) optical flow tracking algorithm is proposed. This enhancement reduces feature‐point matching errors caused by camera displacement and rotation, thereby improving the robustness of the vision front‐end; and (3) The application of BLO‐SLAM to multi‐robot systems. Extensive evaluations on public datasets and self‐constructed datasets demonstrate that the proposed method effectively enhances feature matching efficiency and reduces the influence of irrelevant feature points. As a result, the proposed system improves feature matching accuracy while suppressing the influence of irrelevant feature points, leading to a more robust and reliable map representation.
Qin et al. (Tue,) studied this question.