结构化环境下基于点线特征的VI-SLAM系统
VI-SLAM System Based on Point-line Features in Structured Environment
郭翼彪 1周云水 1黄圣杰 1刘硕 1谢国涛 2秦晓辉2
作者信息
- 1. 湖南大学机械与运载工程学院 长沙 410082
- 2. 湖南大学机械与运载工程学院 长沙 410082;湖南大学无锡智能控制研究院 无锡 214072
- 折叠
摘要
现阶段室内等结构化环境中的机器人定位技术备受关注,视觉-惯性传感器融合的同时定位与建图(visual-inertial simultaneous localization and mapping,VI-SLAM)系统凭借其成本低、体积小、互补性高等优点得到广泛应用.针对现有VI-SLAM系统中相机和IMU的旋转外参在线初始化困难、室内环境中的结构化特征利用不充分等问题,提出一种结构化环境下基于点线特征的双目VI-SLAM系统.该系统基于结构化环境中的线特征,采用先静止、后运动的两步法,在线初始化相机和IMU之间的旋转外参,并通过融合视觉提供的点、线特征的重投影误差约束和IMU提供的预积分约束,共同优化定位系统的状态量.在EuRoC室内无人机数据集和真实地下停车场中的试验表明,两步初始化旋转外参算法有效且准确,可为优化环节提供良好的初始值,通过与多种视觉定位算法进行对比,验证了该系统拥有更高的定位精度.
Abstract
Robot localization technology in structured environments such as indoor has attracted much attention at present.The visual-inertial simultaneous localization and mapping(VI-SLAM)system has been widely used with its low-cost,small-size and high-complementarity.A stereo VI-SLAM system based on point-line features in structured environment is proposed to overcome the difficulty in camera-IMU extrinsic online calibration and insufficient utilization of structured features in the existing VI-SLAM system.Based on the line features in the structured environment,the system uses a two-step method of first stationary and then moving to online initialize the camera-IMU extrinsic parameters,and jointly optimizes the state variables of the localization system by fusing the re-projection error constraints of the point-line features provided by vision and the pre-integration constraints provided by IMU.Experiments on EuRoC indoor UAV datasets and real underground parking lot show that the two-step initialization extrinsic parameters algorithm is effective and accurate to provide good initial value for optimization.Compared with other localization algorithms,the system has higher localization accuracy.
关键词
同时定位与建图/视觉-惯性/点线特征/在线初始化Key words
SLAM/visual-inertial/point-line feature/online initialization引用本文复制引用
基金项目
国家自然科学基金(52102456)
国家自然科学基金(52172384)
长沙市自然科学基金(kq2202162)
汽车车身先进设计制造国家重点实验室开放基金(32115013)
出版年
2024
NETL
NSTL
万方数据