一个同时定位和绘图系统,使用代错误状态卡尔曼波器判断算法,用于全球导航卫星系统
Bo You1,2, Guangjin Zhong1, Chen Chen1,2
1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种强大的自主机器人同时定位和映射 (SLAM) 系统,通过集成LiDAR,IMU和过的GPS数据来提高户外定位的准确性,特别是在GPS信号丢失时.
科学领域:
- 机器人技术和自主系统
- 传感器融合式传感器
- 同时定位和绘制 (SLAM)
背景情况:
- 户外自主移动机器人依赖GPS进行定位,但城市环境和信号损失会导致显著的偏差.
- 现有的SLAM系统与GPS不准确性和信号中断作斗争,限制了可靠的户外导航.
- 准确的定位对于在各种环境中安全高效的自主机器人操作至关重要.
研究的目的:
- 开发一个改进的SLAM系统,解决GPS不准确性和外部自主机器人的信号损失.
- 在具有不可靠GPS数据的具有挑战性的环境中增强定位准确性和稳定性.
- 为在城市和无GPS区域运行的自主移动机器人提供可靠的导航解决方案.
主要方法:
- 提出了一个SLAM系统,结合了Iterated Extended Kalman Filter (IESKF) 和因子图优化.
- 使用LiDAR和惯性测量单元 (IMU) 数据通过IESKF进行前端姿势估计.
- 实现了基于状态和信心的GPS信号过方法,并将处理的GPS,IMU和循环闭合因子集成到后端因子图中.
主要成果:
- 与FAST-LIO和LIO-SAM相比,在KITTI数据集和定制平台上,拟议的SLAM系统显示出更高的本地化准确性.
- 观察到显著的性能改善,特别是在间歇性或丢失GPS信号的场景中.
- 整合过的GPS数据和传感器融合提高了定位系统的整体稳定性.
结论:
- 开发的SLAM系统有效地克服了GPS的局限性,为户外自主机器人提供了更高的定位精度和可靠性.
- 拟议的传感器融合和GPS过方法为在具有挑战性的环境中导航提供了强大的解决方案.
- 这项工作有助于在GPS通常不可靠的现实世界应用中推进自主导航能力.
关键词:
全球定位系统 (GPS) 是一个全球定位系统.代错误状态卡尔曼过器 (IESKF)李达尔 (LiDAR) 是一种激光雷达.同时定位和绘制 (SLAM)自主移动机器人 自主移动机器人惯性测量单位 (IMU) 是指惯性测量单位.更多相关视频
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