基于点线特征融合的双目惯性SLAM算法

In indoor weakly textured environment, it is difficult for the SLAM algorithm based on point features to track sufficient effective point features, which leads to low accuracy and robustness, and even causes the system to fail completely. For this problem, a stereo visual SLAM algorithm is proposed based on point and line features and the Inertial Measurement Unit (IMU). The data association accuracy is improved by using the complementation of point and line features, and meanwhile the IMU data is incorporated to provide prior and scale information for the visual localization algorithm. More accurate visual pose is estimated by minimizing multiple residuals function. The environment point and line feature map, dense map and navigation map are then constructed. To overcome the disadvantages of traditional line feature extraction algorithms, which are easy to cause detection of a large number of short and similar line segment features and over-segmentation of line segments in complex scenes. The strategies of length suppression, near line merging and short line chaining are introduced, and an improved FLD algorithm is proposed to reduce the mismatch rate of the line features, and the running speed of the algorithm proposed is more than twice of that of the LSD algorithm. By comparing the simulation results obtained from multiple groups of public datasets and real-world weak texture scenes, it can be seen that the proposed algorithm can obtain richer environment maps with great positioning accuracy and good robustness.