A Robust Visual SLAM System Based on RGB-D Camera Used in Various Indoor Scenes

Generally speaking, visual SLAM systems just based on point features have poor robustness in low-textured scenes, which limits their application. To improve the robustness and accuracy of simultaneous location and mapping in various environments, a multi-scene adaptive visual SLAM system based on RGB-D camera is proposed. We not only use point features in our system, but also introduce line features that are abundant indoors. They are less sensitive to lighting variation and more stable than point features in low-textured scenes. The SLAM system combining point features and line features contains several parts: visual odometry, local mapping, loop closing, full BA and mapping. The experimental results on datasets demonstrate that the performance of our proposed SLAM system is better than state-of-the-art point-based method in various indoor scenes.