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基于扫描上下文优化的紧耦合激光SLAM方法

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高精度地图与定位是无人车实现自主导航作业的重要前提，针对现有松耦合融合方法对观测信息利用不充分，以及传统闭环检测方法匹配精度低等问题，提出一种基于扫描上下文优化的紧耦合激光SLAM方法——optimized SC-LIO-SAM。首先，IMU通过预积分对点云进行去偏校正，同时为激光里程计提供初始位姿估计;激光里程计通过滑动窗口的方法将当前帧的特征点云与局部地图匹配，随后基于扫描上下文的方法对特征点 云进行编码生成点云描述符，实现高效的闭环检测;基于 LIO-SAM的框架，将IMU预积分因子、激光里程计因子、GPS因子以及闭环因子插入全局因子图中，通过基于贝叶斯树的增量平滑优化算法对全局点云优化更新。为了验证所提方法的有效性，采用KITTI数据集评估optimised SC-LIO-SAM的性能，并与 LOAM、LEGO-LOAM以及LIO-SAM对比，结果表明，optimised SC-LIO-SAM相比于LOAM、LEGO-LOAM 以及LIO-SAM等算法，定位精度显著提升。最后将算法应用在开源数据集中，验证了 optimised SC-LIO-SAM能够构建全局一致的地图。
Tightly coupled laser SLAM method base on scan context optimization
High-precision mapping and positioning is an important prerequisite for autonomous navigation of unmanned vehicles,and in view of the insufficient utilization of observation information by existing loosely coupled fusion methods and the low matching accuracy of traditional closed-loop detection methods,a tightly coupled laser SLAM method based on scan context optimization,named optimized SC-LIO-SAM,is proposed.First,the IMU debiases the point cloud through pre-integration and provides initial pose estimation for the laser odometer.The laser odometer matches the feature point cloud of the current frame with the local map by sliding the window.Then,based on the scan context,the feature cloud is encoded to generate point cloud descriptors to achieve efficient closed-loop detection.Based on the LIO-SAM framework,the IMU pre-integration factor,laser odometry factor,GPS factor and closed-loop factor are inserted into the global factor gragh,lastly the global node optimization is updated by using Bayesian tree.In order to verify the effctiveness of the proposed method,the performance of the optimized SC-LIO-SAM is evaluated by KITTI dataset and compared with LOAM,LEGO-LOAM and LIO-SAM,and the experimental results show that the positioning accuracy of the optimized SC-LIO-SAM is significantly improved compared with LOAM,LEGO-LOAM,LIO-SAM and the classical algorithm.Finally,the algorithm is applied to the open-source dataset to prove that the optimized SC-LIO-SAM can build a globally consistent map.

SLAMlaser odometryscan contextpoint cloud descriptorclosed-loop detectionfactor map optimization

汪湘川、张辉、陈波、周熙栋

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长沙理工大学电气与信息工程学院,长沙 410114

湖南大学机器人学院,长沙 410082

SLAM 激光里程计 扫描上下文 点云描述符 闭环检测 因子图优化

科技创新2030-"新一代人工智能"重大项目国家自然科学基金重大研究计划项目国家自然科学基金项目国家自然科学基金项目湖南省科技创新领军人才项目湖南省杰出青年科学基金项目湖南省重点研发计划项目湖南省重点研发计划项目长沙科技重大项目机器人国家重点实验室联合开放基金项目中国高校产学研创新基金项目

2021ZD01145039214820462027810619710712022RC30632021JJ100252021GK40112022GK2011KH20030262021-KF-22-172020HYA06006

2024

10.13195/j.kzyjc.2023.1167

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(10)