Abstract:

Aiming at the requirement of precision in pose recognition process of traditional SLAM（simultaneous localization and mapping）， the reverse optical flow algorithm is added to the front?end part of VINS-Mono. To meet the real?time requirement of SLAM， a marginal optimization algorithm is integrated into the back?end sliding window optimization method of VINS-Mono， the parts excluding camera pose are marginalized firstly， and then pose parts of cameras are marginalized， to accelerate marginalization process. And experiments are carried out using EuRoc（European robotics challenge）datasets. The results show that accuracy is improved slightly by improvement strategy of the front end， and the possible reasons are analyzed. For the improvement strategy of the back end， it is found that compared with the source code， the time used for the marginalization of the improved algorithm is reduced by 25.9% on average， and then compare their trajectory accuracy. It is found that the error is controllable. Finally， it is verified that the improved strategy for the back?end of VINS-Mono is superior in real?time performance.

Key words: state estimation method, sliding window, marginalization, real?time performance, ROS robot simulation platform