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光学 精密工程, 2024, 32 (3): 422, 网络出版: 2024-04-02 激光雷达与惯性测量单元同步融合下的园区三维建图【增强内容出版】Three-dimensional mapping of park based on synchronous fusion of lidar and inertial measurement unitGet PDFFull Text图表MetricsMore 马庆禄 1,*汪军豪 1张杰 1邹政 2
作者单位 1 重庆交通大学 交通运输学院， 重庆4000742 同济大学 道路与交通工程教育部重点实验室， 上海01804
激光雷达 自动驾驶 同步定位与建图 传感器融合 lidar automatic driving synchronous positioning and mapping sensor fusion AI高清视频导读 AI一图读本文 AI语音精读 AI语音超短摘要【AI生成本文一句话精读】：本研究利用激光雷达与惯性测量单元同步融合技术，实现了园区高精度三维建图，有效提升了地图构建的准确性和稳定性。您的浏览器不支持 audio 元素。【AI生成本文短摘要】：本研究利用激光雷达与惯性测量单元的同步融合技术，实现了园区三维建图。该方法通过算法优化和数据融合，提高了建图的精度和稳定性，有效降低了误差和漂移，为自动驾驶等应用提供了更可靠的环境感知基础。您的浏览器不支持 audio 元素。注：本部分内容由 AI 自动生成，仅供您参考。对于您使用本站 AI 自动生成内容所产生的一切后果，本网站及平台运营方概不承担任何商业和法律责任，请您知悉。 摘要针对自动驾驶三维建图中存在的建图不准确以及位姿飘移的问题，利用激光雷达里程计消除惯性测量单元（Inertial Measurement Unit， nIMU）累计误差并通过IMU预积分去除激光雷达点云畸变，形成激光雷达与IMU的紧耦合建图系统；通过增加回环检测因子、激光雷达里程计因子以及IMU预积分因子进行后端图优化，旨在提升定位建图的全局一致性，减小位姿估计误差，降低累计漂移误差。最后，在学校园区实地场景以及利用开源数据集KITTI进行实验验证，实验表明，在选取的学校园区实地场景下，改进算法APE误差均值相较于原算法降低了11.04%，APE均方根误差较于原算法降低了17.35%；改进算法在KITTI数据集场景下平均APE误差下降了10.04%，均方误差方面相较于原算法平均下降了12.04%。研究结果表明，改进的建图方法能够有效提高建图的位姿估计精度与地图构建精度。您的浏览器不支持 audio 元素。 AbstractTo address the issues of inaccurate mapping and position drift in 3D autonomous driving maps， LIDAR odometry was utilized to counteract cumulative errors of the inertial measurement unit（IMU）， and corrections for LIDAR point cloud distortions were made through IMU pre-integration. This approach enabled the creation of a mapping system where LIDAR and IMU were tightly integrated. Subsequently， the back-end map was enhanced by the incorporation of loopback detection， LIDAR odometry， and IMU pre-integration factors， aiming to bolster the global consistency of the positioning map and minimize cumulative drift errors. The optimization of the back-end map sought to enhance global localization consistency， reduce positioning errors， and curtail cumulative drift. Experimental validation was conducted in a school campus environment and with the use of the KITTI open-source dataset. The results demonstrate that in the school campus scenario， an 11.04% reduction in average APE error and a 17.35% decrease in RMSE are achieved by the refined algorithm compared to the baseline algorithm. For the KITTI dataset scenario， a reduction of 10.04% in both average APE error and RMSE， and a 12.04% decrease in mean square error are observed， underscoring the efficacy of the enhanced mapping technique in elevating position estimation and map construction precision. PDF全文 马庆禄, 汪军豪, 张杰, 邹政. 激光雷达与惯性测量单元同步融合下的园区三维建图[J]. 光学 精密工程, 2024, 32(3): 422. Qinglu MA, Junhao WANG, Jie ZHANG, Zheng ZOU. Three-dimensional mapping of park based on synchronous fusion of lidar and inertial measurement unit[J]. Optics and Precision Engineering, 2024, 32(3): 422.
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