电子测量与仪器学报

邓鹏,罗静.复杂环境下机器人多传感器融合定位方法[J].电子测量与仪器学报,2023,37(12):48-57

复杂环境下机器人多传感器融合定位方法

Robot multi-sensor fusion localization method in complex environment

DOI：

英文关键词:multi-sensor fusion complex environment ground point cloud extraction key frame selection strategy

基金项目:荆门市科技计划项目(2023YFYB040)、湖北省高等学校优秀中青年科技创新团队项目( T2021028)、荆楚理工学院教学研究项目(JX2023-014)资助

作者	单位
邓鹏	1.荆楚理工学院电子信息工程学院
罗静	1.荆楚理工学院电子信息工程学院

Author	Institution
Deng Peng	1.School of Electronic Information Engineering, Jingchu University of Technology
Luo Jing	1.School of Electronic Information Engineering, Jingchu University of Technology

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中文摘要:

为了解决在斜坡、特征退化以及 GNSS 信号丢失等复杂环境下连续精确的定位问题,提出了基于地面约束的多传感器融合方案,用于提高 SLAM 算法的整体性能。首先提出了不同系统状态下的关键帧选取策略。通过在起始位置增加关键帧的数量,避免了因子图优化后产生的定位跳变,从而得到连续准确的位姿输出。同时,针对误差累积所导致回环检测失效,利用该关键帧策略,有效地增大当前帧的子关键帧集合,提高了回环检测算法的鲁棒性。其次,针对 IMU 在长时间运行后高度方向上漂移过大的问题,本文根据提取的地面点构建地面约束,并引入因子图中进行优化。最后,利用搭建的移动机器人实验平台,完成了校园不同场景的数据采集,验证本文算法的有效性,并在 KITTI 数据集与 LIO-SAM 算法进行了对比测试,通过误差分析表明本文算法具有更优的定位精度。

英文摘要:

In order to solve the problem of continuous and accurate localization in complex environments such as slope, feature degradation and GNSS signal loss, a multi-sensor fusion scheme based on ground constraints is proposed in this paper to improve the overall performance of SLAM algorithm. Firstly, the key frame selection strategy under different system states is proposed. By increasing the number of key frames in the starting position, the positioning jump caused by factor map optimization is avoided, and continuous and accurate pose output is obtained. At the same time, in order to prevent the loopback detection failure caused by error accumulation, this keyframe strategy is used to effectively increase the subkeyframe set of the current frame, and improve the robustness of the loopback detection algorithm. Secondly, to solve the problem that IMU drifts too much in the height direction after long-term operation, this paper constructs the ground constraint according to the extracted ground points and introduces it into the factor graph for optimization. Finally, the mobile robot experiment platform is used to complete the data collection of different scenes on campus, and the effectiveness of the proposed algorithm is verified. The comparison test between KITTI data set and LIO-SAM algorithm is carried out, and the error analysis shows that the proposed algorithm has better positioning accuracy.

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