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.

翻译：本文针对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）的定位系统开发难题展开研究。其目标是实现精确地图构建与远程导航，并在自主施工、军事工程任务及地外行星探测等实际领域具备应用价值。所提出的系统——地形参考保障工程定位系统（TRAELS）——通过扩展卡尔曼滤波器（EKF）融合了两种互补性地形参考导航（TRN）方法生成的位姿估计、轮式里程计及惯性测量单元（IMU）数据。与需要回环检测的同步定位与地图构建（SLAM）系统不同，本方法无需重复访问先前位置即可在长距离单向任务中保持精度。TRAELS在多种环境下进行了评估。在同时具备独特几何与地表特征的区域，TRAELS利用所开发的TRN方法持续实现了低于3.0米的绝对轨迹误差。实验还表明，该方法在穿越特征稀疏区域时能够恢复大量累积漂移，这对确保系统在各类具有挑战性的GNSS拒止环境中的鲁棒性能至关重要。总体而言，本研究验证了该系统在严苛GNSS拒止环境下提供精准定位与制图能力的有效性，并通过分析为改进UGV的TRN方法提供了见解。