PLD-VINS: RGBD visual-inertial SLAM with point and line features

Abstract

It is a big challenge for autonomous robots to estimate state efficiently and generate high-precision 3D maps under low texture indoor scenes. This paper proposes a visual-inertial simultaneous localization and mapping (SLAM) system with point features, line features and depth information provided by RGBD camera, which is named as PLD-VINS. The main advantage of PLD-VINS is that it can improve the accuracy of state estimation and dense 3D mapping with an RGBD camera. Firstly, line features are added to local state estimation to improve the accuracy of relative state estimation between keyframes, which differs from most of existing solutions that only rely on point features. Secondly, an improved EDLines algorithm is introduced to improve the quality and efficiency of line segment detection, and compensate disadvantages of traditional line feature detection algorithms, which often detect short or similar line segments, and are also over-segmentation in complicated scenes. Thirdly, optical flow method is employed to track the detected line segment, which can effectively reduce the cost of calculation and improve the efficiency of the proposed system. Finally, 3D point cloud maps are built which can be input to different kinds of post-processes. The performance of PLD-VINS is validated on public OpenLORIS-Scene datasets and real-world experiments. Comparing with other state-of-the-art algorithms, such as ORB-SLAM2, PL-VINS, VINS-RGBD, and so on, the proposed PLD-VINS is more exact, robust and reliable.