  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.
