Point-line-based RGB-D SLAM and Bundle Adjustment Uncertainty Analysis
This work addresses robustness issues in SLAM for urban and indoor environments, but it is incremental as it builds on existing point-line feature methods.
The paper tackles the problem of sparse point features in low-textured scenes for visual SLAM by combining point and line features from RGB-D data, resulting in improved robustness and accuracy in challenging environments as demonstrated on public datasets.
Most of the state-of-the-art indirect visual SLAM methods are based on the sparse point features. However, it is hard to find enough reliable point features for state estimation in the case of low-textured scenes. Line features are abundant in urban and indoor scenes. Recent studies have shown that the combination of point and line features can provide better accuracy despite the decrease in computational efficiency. In this paper, measurements of point and line features are extracted from RGB-D data to create map features, and points on a line are treated as keypoints. We propose an extended approach to make more use of line observation information. And we prove that, in the local bundle adjustment, the estimation uncertainty of keyframe poses can be reduced when considering more landmarks with independent measurements in the optimization process. Experimental results on two public RGB-D datasets demonstrate that the proposed method has better robustness and accuracy in challenging environments.