HE-SLAM: a Stereo SLAM System Based on Histogram Equalization and ORB Features
This addresses robustness issues in visual SLAM for real-world applications like robotics, but it is incremental as it builds on existing ORB-SLAM2 with a preprocessing step.
The paper tackles the problem of visual SLAM failing in low-contrast environments by proposing HE-SLAM, which combines histogram equalization with ORB features to improve robustness, achieving competitive performance on datasets like KITTI and EuRoc compared to ORB-SLAM2.
In the real-life environments, due to the sudden appearance of windows, lights, and objects blocking the light source, the visual SLAM system can easily capture the low-contrast images caused by over-exposure or over-darkness. At this time, the direct method of estimating camera motion based on pixel luminance information is infeasible, and it is often difficult to find enough valid feature points without image processing. This paper proposed HE-SLAM, a new method combining histogram equalization and ORB feature extraction, which can be robust in more scenes, especially in stages with low-contrast images. Because HE-SLAM uses histogram equalization to improve the contrast of images, it can extract enough valid feature points in low-contrast images for subsequent feature matching, keyframe selection, bundle adjustment, and loop closure detection. The proposed HE-SLAM has been tested on the popular datasets (such as KITTI and EuRoc), and the real-time performance and robustness of the system are demonstrated by comparing system runtime and the mean square root error (RMSE) of absolute trajectory error (ATE) with state-of-the-art methods like ORB-SLAM2.