Monocular inertial indoor location algorithm considering point and line features

Abstract

Compared with point features, line features in the environment have more structural information. When indoor texture is not rich, making full use of the structural information of line features can improve the robustness and accuracy of simultaneous location and mapping algorithm. In this paper, we propose an improved monocular inertial indoor location algorithm considering point and line features. Firstly, the point features and line features in the environment are extracted, matched and parameterized, and then the inertial sensor is used to estimate the initial pose, and the tightly coupled method is adopted to optimize the observation error of the point and line features and the measurement error of the inertial sensor simultaneously in the back optimization to achieve accurate estimation of the pose of unmanned aerial vehicle. Finally, loop closure detection and pose graph optimization are used to optimize the pose in real time. The test results on public datasets show that the location accuracy of the proposed method is superior to 10 cm under sufficient light and texture conditions. The angle measurement accuracy is better than 0.05 rad, and the output frequency of positioning results is 10Hz, which effectively improves the accuracy of traditional visual inertial location method and meets the requirements of real-time.