基于ORB-SLAM3的PROSAC双目优化补偿SLAM

Binocular SLAM Optimization Compensation Using PROSAC Based on ORB-SLAM3

为优化无人机SLAM系统运行过程中发生的跟踪失败导致局部建图失败以及双目摄像头在远近复杂场景下轨迹误差大的现象,提出了一种双目SLAM。首先在系统中加入新的约束优化双目匹配的视差与深度,然后应用光流法基于单应运动补偿原理和PROSAC算法去除误匹配,最后根据单应补偿结果投影得到新的数据关联,在关键帧和地图点应用新的手段提高对补偿结果的利用率,保证了系统的实时性和鲁棒性。所提算法利用双目视差优化计算关键点的深度从而提高系统的定位精度和系统鲁棒性,在室内无人机EuRoC数据集上取得了较为优秀的精度提升。

In order to optimize the problem of tracking failure during the operation of the UAV SLAM system which leads to the failure of local mapping and optimize the problem of large trajectory errors of the binocular camera in far and near complex scenes this paper proposes a binocular SLAM.Firstly new constraints are added to the system to optimize the disparity and depth of binocular matching then the optical flow method is applied to remove the mismatches based on homography motion compensation and PROSAC algorithm and finally new data associations are obtained by projection according to the homography compensation results.A new method is applied to the key frames and map points to improve the utilization rate of compensation results and ensure the real-time performance and robustness of the system.The proposed algorithm uses binocular parallax to optimize the calculation of the depth of key points to improve the positioning accuracy and robustness of the system and achieves excellent accuracy improvement on the EuRoC data set of indoor UAVs.