一种点线特征融合的双目同时定位与地图构建方法

基于特征点的视觉同时定位与构图方法依赖于图像质量以及可提取的特征点数量,且稀疏的特征点不能直观表达环境的结构信息。为此,提出一种将图像的点特征和线段特征融合的双目同时定位与构图方法。算法前端提取图像的点特征和线段特征,进行特征跟踪并完成相机位姿求解,从跟踪线程中分离出特征提取线程,进一步提升了前端线程的帧率。后端采用集束调整对局部地图进行优化,利用基于词袋模型的闭环检测以抑制系统的累积误差。最后结合点线特征共同构建环境地图。在公开数据集上进行了实验,与当前主流算法相比,提出的算法在保证定位精度的同时能够获得更丰富的环境地图,具备较好的鲁棒性与实时性。

A Stereo Simultaneous Localization and Mapping Method Based on Point-Line Feature Fusion

Feature-based visual Simultaneous Localization and Mapping methods depends on the image quality and the number of feature points that can be extracted, and the sparse feature points cannot visually express the structural information of the environment. Therefore, a stereo visual SLAM method combining point features and line segment features is proposed. In the front end of the algorithm, the point features and line segment featuresare extracted, and then feature tracking is performing to solve the camera pose. The feature extraction thread is separated from the trace thread, which further improves the frame rate of the front-end thread. In the back end,bundle adjustment is used to optimize the local mapping. And a closed-loop detection method based on bag-of-words model is to suppress the accumulated error of the system. Finally, the environment map is constructed by combining with the points-lines. Compared with the current mainstream algorithms, experiments on the public dataset show that the proposed SLAM algorithm can obtain a richer environment map whileensuring the positioning accuracy, and has better robustness.