基于点线特征融合的双目惯性SLAM算法

Stereo visual-inertial SLAM algorithm based on merge of point and line features

针对室内弱纹理场景下,基于点特征的SLAM算法难以追踪足够多的有效特征点,导致系统定位精度和鲁棒性较差,甚至完全失效的问题,提出一种基于点线特征并融合惯性测量单元(IMU)的双目视觉惯性SLAM算法。利用点线特征的互补优势来提高数据关联的准确性,同时引入IMU数据为视觉定位算法提供先验和尺度信息,通过联合最小化多残差函数得到更准确的相机位姿,并据此构建环境点线特征地图、稠密地图和导航地图。针对传统线特征提取算法在复杂场景下易检测到大量短线段和相似线段特征以及线段存在过分割等弊端,利用线段长度抑制、近线合并及断线拼接策略在FLD算法的基础上进行改进,以降低线特征的误匹配率,运行速度是LSD算法的2倍以上。通过对比多组公开数据集和真实弱纹理场景下得到的仿真实验结果可知,所提算法在保证定位精度的同时能够获得更为丰富的环境地图,具备较好的鲁棒性。

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.