Tightly coupled LiDAR-inertial SLAM based on vertical constraint
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维普
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为了解决现有激光SLAM(simultaneous localization and mapping)方法忽略垂直方向漂移而导致的高度不准确和地图重影问题,提出了一种基于垂直约束的紧耦合激光惯性SLAM方法.该方法结合激光雷达传感器的安装高度以及点到激光雷达的距离提取精确的地面点,基于提取的地面点设计了一种考虑垂直方向残差的激光里程计,使用两步列文伯格-马夸尔特(Levenberg-Marquardt,L-M)方法来求解姿态变换,这些残差将有助于在垂直方向上收敛到最优解.使用简单有效的基于欧氏距离的回环检测方法避免地图重影问题.为验证算法的优越性,在KITTI数据集及真实场景下均进行了相关实验.在KITTI数据集上,与LeGO-LOAM、LIO-SAM和Point-LIO相比,轨迹均方根误差(root mean square error,RMSE)分别降低了47.62%、33.14%和73.79%.在实测校园环境中,与LeGO-LOAM、LIO-SAM和Point-LIO相比,RMSE分别降低了83.56%、13.55%和82.04%,从而验证了提出方法具有更高的定位精度.
To address the issues of height inaccuracy and ghost map caused by the vertical drift overlooked in existing LiDAR SLAM methods,a tightly coupled LiDAR-inertial SLAM method based on vertical constraints is proposed.Proposed method extracts precise ground points by combining the installation height of the LiDAR sensor and the distance from points to the LiDAR.Based on the extracted ground points,a LiDAR odometry considering vertical residuals is designed.Proposed method uses a two-step Levenberg-Marquardt(L-M)method to solve for pose transformation.These residuals contribute to converging to the optimal solution in the vertical direction.A native but effective Euclidean distance-based loop closure detection method is used to avoid ghost map.To verify the superiority of the proposed algorithm,relevant experiments were conducted on the KITTI dataset and in real-world environments.On the KITTI dataset,the root mean square error(RMSE)of the trajectories obtained by the proposed algorithm were reduced by 47.62%,33.14%,and 73.79%compared to LeGO-LOAM,LIO-SAM,and Point-LIO,respectively.In real-world campus environments,the RMSE of the trajectories obtained by the proposed algorithm were reduced by 83.56%,13.55%,and 82.04%compared to LeGO-LOAM,LIO-SAM,and Point-LIO,respectively.These results demonstrate the higher localization accuracy of the proposed method.
维普
万方数据
