Abstract
It's a practical approach using the ground-aerial collaborative system to enhance the localization robustness of flying robots in cluttered environments, especially when visual sensors degrade. Conventional approaches estimate the flying robot's position using fixed cameras observing pre-attached markers, which could be constrained by limited distance and susceptible to capture failure. To address this issue, we improve the ground-aerial localization framework in a more comprehensive manner, which integrates active vision, single-ranging, inertial odometry, and optical flow. First, the designed active vision subsystem mounted on the ground vehicle can be dynamically rotated to detect and track infrared markers on the aerial robot, improving the field of view and the target recognition with a single camera. Meanwhile, the incorporation of single-ranging extends the feasible distance and enhances re-capture capability under visual degradation. During estimation, a dimension-reduced estimator fuses multi-source measurements based on polynomial approximation with an extended sliding window, balancing computational efficiency and redundancy. Considering different sensor fidelities, an adaptive sliding confidence evaluation algorithm is implemented to assess measurement quality and dynamically adjust the weighting parameters based on moving variance. Finally, extensive experiments under conditions such as smoke interference, illumination variation, obstacle occlusion, prolonged visual loss, and extended operating range demonstrate that the proposed approach achieves robust online localization, with an average root mean square error of approximately 0.09 m, while maintaining resilience to capture loss and sensor failures.
Abstract (translated)
这是一种实用的方法，利用地面-空中协作系统来增强飞行机器人在复杂环境中定位的鲁棒性，特别是在视觉传感器性能下降的情况下。传统方法通常使用固定摄像头观测预先附着的标记来估计飞行机器人的位置，这种方法受限于观察距离，并且容易因捕捉失败而失效。为了解决这个问题，我们从更全面的角度改进了地面-空中定位框架，该框架整合了主动视觉、单程测距、惯性里程计和光学流。 首先，安装在地面上的车辆上的设计好的主动视觉子系统可以动态旋转以检测和跟踪飞行机器人上红外标记的位置，从而提高视野范围并用单一摄像头实现目标识别。与此同时，引入单程测距技术扩展了可行距离，并且增强了在视觉传感器性能下降时重新捕捉的能力。 在估计过程中，采用一种基于多项式逼近的缩减维数估计算法，在一个延伸的时间窗口内融合多源测量值，从而平衡计算效率和冗余度。考虑到不同传感器的数据精确性差异，我们实施了一种自适应滑动置信评估算法来评估测量质量，并根据移动方差动态调整权重参数。 最后，在烟雾干扰、光照变化、障碍物遮挡、长时间视觉丢失以及扩展操作范围等条件下进行了广泛的实验，结果显示所提出的方案实现了鲁棒的在线定位，平均均方根误差约为0.09米，同时保持了对捕捉失败和传感器故障的抗性。
URL
https://arxiv.org/abs/2512.16367
