一种在非结构化室外GNSS拒止环境中无人地面车辆的鲁棒定位解决方案
A Robust Localization Solution for an Uncrewed Ground Vehicle in Unstructured Outdoor GNSS-Denied Environments
AI总结：
本研究提出TRAELS系统，融合地形参考导航、轮式里程计和IMU数据，通过EKF滤波，在GNSS拒止的非结构化户外环境中实现UGV的精确鲁棒定位与测绘，无需回环检测。
摘要：
这项工作解决了为在非结构化室外全球导航卫星系统（GNSS）拒止环境中自主运行的无人地面车辆（UGV）开发定位系统所面临的挑战。目标是实现精确的测绘和远程导航，并在自主施工、军事工程任务以及非地球行星探索等领域具有实际应用价值。所提出的系统——地形参考可靠工程定位系统（TRAELS）——利用扩展卡尔曼滤波器（EKF）将两种互补的地形参考导航（TRN）方法产生的位姿估计与车轮里程计和惯性测量单元（IMU）测量值进行融合。与需要回环检测的同步定位与建图（SLAM）系统不同，所描述的方法在长距离和单程任务中保持精度，而无需重新访问先前位置。TRAELS的评估在多种环境中进行。在具有独特几何和地面表面特征组合的区域，TRAELS利用所开发的TRN方法持续实现绝对轨迹误差小于3.0米。该方法还被证明能够在穿越特征稀疏区域时从大的累积漂移中恢复，这对于确保系统在多种具有挑战性的GNSS拒止环境中的鲁棒性能至关重要。总体而言，该系统在具有挑战性的GNSS拒止环境中提供精确定位和测绘能力的有效性得到了证明，并进行了分析，得出了改进UGV的TRN方法的见解。
英文摘要：
This work addresses the challenge of developing a localization system for an uncrewed ground vehicle (UGV) operating autonomously in unstructured outdoor Global Navigation Satellite System (GNSS)-denied environments. The goal is to enable accurate mapping and long-range navigation with practical applications in domains such as autonomous construction, military engineering missions, and exploration of non-Earth planets. The proposed system - Terrain-Referenced Assured Engineer Localization System (TRAELS) - integrates pose estimates produced by two complementary terrain referenced navigation (TRN) methods with wheel odometry and inertial measurement unit (IMU) measurements using an Extended Kalman Filter (EKF). Unlike simultaneous localization and mapping (SLAM) systems that require loop closures, the described approach maintains accuracy over long distances and one-way missions without the need to revisit previous positions. Evaluation of TRAELS is performed across a range of environments. In regions where a combination of distinctive geometric and ground surface features are present, the developed TRN methods are leveraged by TRAELS to consistently achieve an absolute trajectory error of less than 3.0 m. The approach is also shown to be capable of recovering from large accumulated drift when traversing feature-sparse areas, which is essential in ensuring robust performance of the system across a wide variety of challenging GNSS-denied environments. Overall, the effectiveness of the system in providing precise localization and mapping capabilities in challenging GNSS-denied environments is demonstrated and an analysis is performed leading to insights for improving TRN approaches for UGVs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
