摘要
随着国家深地能源战略和地下基础工程的部署展开,自主移动机器人在地下矿山、工程隧道和地下管道等领域的需求快速增长。地下自主作业机器人所处环境复杂,普遍面临卫星定位信号拒止和场景退化特征,导致机器人位姿状态估计误差漂移严重、环境地图构建扭曲变形。针对地下退化环境机器人状态估计不完备的问题,提出一种精准、鲁棒的激光雷达-惯性同时定位与建图(SLAM)框架和方法,组合惯性里程计和激光雷达-惯性里程计级联优化过程,并在激光雷达点云特征匹配中引入强度特征降低点云几何特征稀疏引起的匹配误差,并通过退化检测引入正确的约束方向,保证位姿估计信息的鲁棒性和准确性。公开数据集和现场巷道实验结果表明,所提方法在精度、鲁棒性方面均有出色表现,在地下巷道退化环境的定位精度可达0.03 m,可为地下退化环境机器人提供可靠的状态估计和环境描述。
With the deployment of the national deep-earth energy strategy and underground infrastructure projects in China,the demand for autonomous mobile robots in underground mines,engineering,and pipelines is growing rapidly.Underground autonomous robots have to bear troubles like satellite positioning signal denial and scene degradation which easily lead to serious error drift in robot pose estimation and distortion in environmental map construction.To address the problem of incomplete state estimation of underground degraded environment robots,an accurate and robust LiDAR-inertial SLAM framework and method is proposed.It combines the inertial odometer and the LiDAR-inertial odometer by the cascade optimization process.In addition,the intensity feature is introduced into LiDAR point cloud feature matching to reduce the matching error caused by sparse point cloud geometric features,and correct constraint direction is introduced through degradation detection to ensure the robustness and accuracy of pose estimation.The experimental results on public datasets and field tunnels show that the proposed method has excellent performance both in accuracy and robustness.The positioning accuracy in the degraded roadway reaches 0.03 m,which can provide reliable state estimation and environment description for robots in underground degraded environments.
作者
崔玉明
刘送永
吕振礼
李洪盛
王崧全
Cui Yuming;Liu Songyong;Lyu Zheni;Li Hongsheng;Wang Songquan(School of Mechatronic Engineering,Jiangsu Normal University,Xuzhou 221116,China;School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China;School of Mechanical&Electrical Engineering,Xuzhou University of Technology,Xuzhou 221018,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2023年第12期208-216,共9页
Chinese Journal of Scientific Instrument
基金
江苏省自然科学基金青年基金(BK20230688)
江苏省杰出青年基金(BK20211531)
江苏省高等学校基础科学(自然科学)研究项目(22KJB440004)
徐州市重点研发计划项目(KC22404)
江苏师范大学博士学位教师科研支持项目(22XFRS011)资助。
关键词
自主定位
激光雷达-惯性里程计
强度特征
级联优化
autonomous positioning
LiDAR-inertial odometer
intensity feature
cascade optimization
