Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) constrained aerial robots operating in perceptually-degraded GPS-denied environments. We can, however, actively increase the amount of perceptual information available to such robots by augmenting them with a small number of more expensive, but less resource-constrained, agents. Specifically, the latter can serve as sources of perceptual information themselves. In this paper, we study the problem of optimally positioning (and potentially navigating) a small number of more capable agents to enhance the perceptual environment for their lightweight,inexpensive, teammates that only need to rely on cameras and IMUs. We propose a numerically robust, computationally efficient approach to solve this problem via nonlinear optimization. Our method outperforms the standard approach based on the greedy algorithm, while matching the accuracy of a heuristic evolutionary scheme for global optimization at a fraction of its running time. Ultimately, we validate our solution in both photorealistic simulations and real-world experiments. In these experiments, we use lidar-based autonomous ground vehicles as the more capable agents, and vision-based aerial robots as their SWAP-constrained teammates. Our method is able to reduce drift in visual-inertial odometry by as much as 90%, and it outperforms random positioning of lidar-equipped agents by a significant margin. Furthermore, our method can be generalized to different types of robot teams with heterogeneous perception capabilities. It has a wide range of applications, such as surveying and mapping challenging dynamic environments, and enabling resilience to large-scale perturbations that can be caused by earthquakes or storms.

翻译：准确且鲁棒的状态估计对机器人团队的自主导航至关重要。对于在感知退化且无GPS环境中运行的大规模、受尺寸、重量和功率（SWAP）限制的空中机器人群体而言，这一任务尤为艰巨。然而，我们可以通过引入少量成本更高但资源约束较小的智能体来主动增加此类机器人可用的感知信息量。具体而言，后者可自身作为感知信息的来源。本文研究了如何优化定位（及潜在导航）少量能力更强的智能体，以增强其仅依赖摄像头和惯性测量单元（IMU）的轻量级、低成本队友的感知环境。我们提出了一种数值鲁棒、计算高效的非线性优化方法来解决该问题。该方法优于基于贪心算法的标准方法，同时以极低的运行时间开销匹配了启发式全局优化进化方案的精度。最终，我们在光度逼真仿真和真实实验中验证了该方案。实验中，我们以基于激光雷达的自主地面车辆作为能力更强的智能体，以视觉感知的空中机器人作为受SWAP限制的队友。该方法可将视觉惯性里程计的漂移降低高达90%，且显著优于随机放置激光雷达装备智能体的策略。此外，该方法可推广至具有异构感知能力的多种机器人团队，具有广泛的应用场景，例如对复杂动态环境进行测绘，以及增强对地震或风暴等大规模扰动事件的鲁棒性。