Factor Graph-based Laser SLAM Model Optimization Algorithm
For the study of laser SLAM point cloud mapping model,a factor graph optimized SLAM model scheme is designed.In the front-end module,the laser odometry realizes the tight coupling of IMU and radar point cloud data by introducing IESKF,thus IESKF-LIO being constructed.In the SLAM back-end model,a variety of factors are built for pose constraints and compensation optimization in order to improve the robustness and real-time accuracy of SLAM,meanwhile,key frames and incremental smooth mapping are proposed during the factor graph fusion process to reduce the computational burden of the model.Through the mapping experiments in KITTI data sets,the experimental results verify that the designed model has lower trajectory errors and better mapping effects than the traditional SLAM onel.
万方数据
维普
