工业园区的强大定位UGV和先前的地图维护维护
Fanrui Luo1, Zhenyu Liu1, Fengshan Zou2
1School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了一种改进的LiDAR-IMU测距方法,用于在工业区精确地定位无人地面车辆 (UGV),提高准确性和效率,同时减少地图维护资源的使用.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 在没有GPS的工业环境中,精确定位无人地面车辆 (UGV) 是一个挑战.
- 现有的同时定位和映射 (SLAM) 方法在距离偏移和运动不连续性方面扎.
- 强大的实时定位对于复杂工业环境中的自主导航至关重要.
研究的目的:
- 为了提高工业园区UGV的实时定位准确性和效率.
- 为了提高UGV测距器的稳定性,减轻运动控制中断和漂移.
- 为减少资源消耗开发一种新的先前地图维护方法.
主要方法:
- 一种基于FAST-LIO2的紧密合的LiDAR-IMU测距方法,集成地面约束和一种新的特征提取技术.
- 一个双向投射平面斜率差异过器,用于高效准确的点云特征提取 (边缘,平面,地面).
- 一个层次和分区的先前地图维护策略,用于实时更新.
主要成果:
- 与基线FAST-LIO2.2相比,KITTI数据集的测距精度提高了30.19%和48.24%.
- 在KITTI数据集上,公里计效率提高了56.72%和40.06%.
- 通过先前的地图实现了厘米级的定位精度,并在z轴上达到毫米级的精度.
- 通过提议的先前地图维护方法,RAM使用量减少了64%.
结论:
- 拟议的紧密合的LiDAR-IMU测距方法显著提高了工业环境中的UGV定位精度和效率.
- 新的特征提取和预先的地图维护方法有助于强大和资源高效的自主导航.
- 该方法展示了厘米级定位精度,对于没有GPS的工业应用至关重要.
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