基于误匹配剔除和地面约束的视觉SLAM算法
Visual SLAM algorithm based on mismatch rejection and ground constraints
黄丹丹 1郝文豪 1杨阳1
作者信息
- 1. 长春理工大学 电子信息工程学院,长春 130000
- 折叠
摘要
为了提高视觉同时定位与建图(SLAM)系统的鲁棒性和准确性,提出了一种基于误匹配剔除和地面约束的视觉SLAM算法.首先,在SLAM前端引入特征点对剔除机制,通过统计特征点对之间的距离以及分布特性对匹配质量进行分析,进而实现误匹配特征点的剔除;其次,利用RGBD相机提取地面向量,并将地面向量约束引入SLAM后端优化过程中,能够有效防止z轴过度优化以及抑制z轴漂移,提高位姿优化的准确性;最后,在公开数据集上进行实验和分析.实验结果表明,与 ORB-SLAM2 算法相比,使用误匹配剔除算法的绝对轨迹误差平均减少了 17.62%,使用地面约束算法的绝对轨迹误差平均减少了 39.20%,验证了所提算法具有更好的准确性和鲁棒性.
Abstract
In order to improve the robustness and accuracy of the visual simultaneous localization and mapping(SLAM)system,a visual SLAM algorithm based on mismatch rejection and ground constraints is proposed.Firstly,the feature point pair rejection mechanism is introduced in the front-end of SLAM,and the matching quality is analyzed by counting the distances between feature point pairs and the distribution characteristics,and then the mismatched feature points are eliminated.Secondly,the RGBD camera is used to extract ground vectors,and the ground vector constraints are introduced into the back-end optimization process of SLAM,which can effectively prevent over-optimization of the z-axis and suppress the drifting of z-axis,and improve the accuracy of position optimization.Finally,the experiment and analysis are carried out on the public datasets.The experimental results show that compared with the ORB-SLAM2 algorithm,the absolute trajectory error of the mismatch rejection algorithm is reduced by 17.63%on average,and the absolute trajectory error of the ground constraint algorithm is reduced by 39.20%on average,which verifies that the proposed algorithm has better accuracy and robustness.
关键词
机器视觉/同时定位与建图/特征点剔除/后端优化/地面约束Key words
machine vision/simultaneous localization and mapping/feature point rejection/back-end optimization/ground constraints引用本文复制引用
基金项目
国家重大科研仪器研制项目(62127813)
吉林省科技厅重点研发项目(202302011071GX)
出版年
2024
NETL
NSTL
万方数据