发表机构
TU Delft; European Space Agency（代尔夫特理工大学; 欧洲空间局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对行星无人机定位受有效载荷限制的问题，提出系绳-惯性定位方法，结合解析悬链线模型与GP残差补偿，误差达5.2厘米，优于现有技术，可替代视觉与GNSS定位。
AI 中文摘要
行星探测的最新进展已展现出无人机（UAV）的潜力，例如机智号直升机提供了宝贵的测绘数据。然而，有限的有效载荷能力限制了飞行时间和定位可用的计算资源，从而限制了其适用性。通过提供系绳连接，电池和计算约束等问题被转移到基础漫游车。同时，缆线可被用于实现无漂移的定位。本研究提出了一种新颖的系绳-惯性定位方法，该方法利用系绳长度和角度测量值来估计无人机相对于其基地的位置。该方法将计算高效的解析悬链线模型与高斯过程（GP）残差误差补偿相结合，以解决系统性传感器误差和模型局限性。针对圆形、三角形和八字形轨迹的实验验证，系绳长度最大为4.5米，总飞行时间为37分钟，证明了所提方法的有效性。仅使用基于系绳的位置估计作为反馈，解析悬链线模型的平均均方根误差（RMSE）为7.4厘米，通过基于GP的残差补偿，该误差进一步降低至5.2厘米，比现有技术水平高出一个数量级。这些结果确立了系绳-惯性定位作为视觉和GNSS定位的实用替代方案，适用于系绳无人机（TUAVs）。
英文摘要
Recent developments in planetary exploration have shown the potential of Unmanned Aerial Vehicles (UAVs), such as the Ingenuity helicopter that provided valuable mapping data. However, limited payload capabilities constrain the flight times and compute available for localization, which restrict their applicability. By providing a tethered connection, issues such as battery and computational constraints are offloaded to the base rover. At the same time, the cable can be exploited for non-drifting localization. This work presents a novel Tether-Inertial Localization approach that uses tether length and angle measurements to estimate the UAV position relative to its base. The method combines a computationally efficient analytical catenary model with a Gaussian Process (GP) residual error compensation. This accounts for systematic sensor inaccuracies and model limitations. Experimental validation across circular, triangular, and figure-eight trajectories with tether lengths up to 4.5 m and a total flight time of 37 minutes demonstrates the effectiveness of the proposed approach. Using only tether-based position estimates for feedback, the analytical catenary model achieves an average RMSE of 7.4 cm, which is further reduced to 5.2 cm through GP-based residual compensation, one order of magnitude better than the state-of-the-art. These results establish Tether-Inertial Localization as a practical alternative to vision- and GNSS-based localization for Tethered Unmanned Aerial Vehicles (TUAVs).
