A SLAM Pose Graph Optimization Method Using Dual Visual Odometry

Backend trajectory optimization is an important part of the visual simultaneous localization and map-ping system,which can significantly improve localization accuracy.However,the existing optimization methods based on the bundle adjustment have a large amount of calculation in large scenes and cannot be applied to end-to-end visual odometries.To solve this problem,a universal backend pose graph optimization algorithm with two visual odometries at the front end is proposed,which can be applied to end-to-end visual odometries.This method uses a high-speed but low-precision end-to-end visual odometry to run at high frequency,while a low-speed but high-precision visual odometry runs at a low frequency.Local optimization uses Gauss-Newton method iterative optimization through the constraints provided by two odometries.Global optimization is per-formed simultaneously which based on key frames scene matching.Experiments show that the simultaneous lo-calization and mapping system which apply this optimization method can run in real-time on the KITTI dataset.Compared with the two visual odometries,the accuracy has been greatly improved.And compared with several well-known open source simultaneous localization and mapping methods that apply backend trajectory opti-mization,low errors have been achieved in trajectory error,absolute translational error,rotation error and rela-tive pose error,taking into account the advantages of the accuracy of traditional methods and the advantages of high speed end-to-end methods.In addition,the optimization framework can also be applied to other more visual odometries.