Integrating monocular vision and laser point for indoor UAV SLAM

Abstract

In order to realize the autonomous navigation of an unmanned aerial vehicle while exploring unknown indoor environments and meet the requirements of the indoor UAV positioning and indoor environmental reconstruction, a new strategy is presented based on monocular vision and a laser source. Combining laser-assisted odometry and a scan correlation algorithm with inertial information through an Extended Kaiman Filter (EKF) to realize the purpose localization and mapping. The laser-assisted distance measurement is presented in our paper depends on visual and geometric principle, which has the same effect of laser range finder after error corrections. The pose estimates can be obtained by laser scan matching which is used Sum of Gaussian (SoG) as the essential elements of mapping and localization. At last, in order to improve the autonomy of navigation, EKF is used to fuse inertial information. Experiments in an indoor environment validate the significant engineering reference value of the proposed algorithm.