基于点线特征融合的视觉惯性SLAM算法

针对目前视觉SLAM方法鲁棒性差、耗时高，使系统定位不够精确的问题，提出了一种基于点线特征融合的视觉惯性SLAM算法。首先通过短线剔除和近似线段合并策略改进LSD（line segment detection）提取质量，以提高线特征检测的速率和准确度；然后在后端优化中有效融合了点、线和IMU数据，建立最小化目标函数进行优化，得到更精确的相机位姿；最后在EuRoC数据集和现实走廊场景进行了实验验证。实验表明，所提算法可以有效提升线特征提取的质量和速度，同时有效提高了SLAM系统的定位精度，获得更为丰富的点线结构地图。

Visual inertial SLAM algorithm based on point-line feature fusion

Aiming at the problem that the current visual SLAM method has poor robustness and high time consumption, which makes the system localization not accurately enough, this paper proposed a visual-inertial SLAM algorithm based on point-line feature fusion. Firstly, it improved the LSD extraction quality through short-line culling and approximate line-segment merging strategies to improve the speed and accuracy of line feature detection. Then, it integrated the point, line, and IMU data in the back-end optimization effectively and established the minimized objective function for optimization to obtain a more accurate camera pose. Finally, this paper conducted experimental verification on the EuRoC dataset and real corridor scenes. The experiments show that the proposed algorithm can effectively improve the quality and speed of line feature extraction while effectively improving the localization accuracy of the SLAM system and obtaining a richer point-line structure map.