中国科学院自动化研究所机构知识库

Visual simultaneous localization and mapping (VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry (VIO) is often used as the front-end of VSLAM because of its rich information, lightweight, and robustness. This article proposes the FPL-VIO, an optimization-based fast vision-inertial odometer with points and lines. Traditional VIO mostly uses points as landmarks; meanwhile, most of the geometrical structure information is ignored. Therefore, the accuracy will be jeopardized under motion blur and texture-less area. Some researchers improve accuracy by adding lines as landmarks in the system. However, almost all of them use line segment detector (LSD) and line band descriptor (LBD) in line processing, which is very time-con-suming. This article first proposes a fast line feature description and matching method based on the midpoint and compares the three line detection algorithms of LSD, fast line detector (FLD), and edge drawing lines (EDLines). Then, the measurement model of the line is introduced in detail. Finally, FPL-VIO is proposed by adding the above method to monocular visual-inertial state estimator (VINS-Mono), an optimization-based fast vision-inertial odometer with lines described by midpoint and points. Compared with VIO using points and lines (PL-VIO), the line processing efficiency of FPL-VIO is increased by 3−4 times while ensuring the same accuracy.