通过主动相互观测保持视场受限无人机集群的相对定位
Preserving Relative Localization of FoV-Limited Drone Swarm via Active Mutual Observation
AI总结：
针对视场受限无人机集群中相互观测与环境观测的矛盾，本文提出基于卡尔曼滤波不确定性评估的主动偏航规划定位校正系统，将定位漂移降低最多65%并成功维持编队。
摘要：
相对状态估计对于基于视觉的集群至关重要，用以估计和补偿视觉里程计不可避免的漂移。对于配备最紧凑传感器配置——提供有限视场（FoV）的立体相机——的自主无人机而言，用于相对状态估计的相互观测需求与用于环境观测的需求之间存在冲突。为了在获取相互观测的同时平衡视场受限集群的这两种需求并保证安全性，本文提出了一种主动定位校正系统，该系统在飞行过程中通过偏航规划器规划相机朝向。偏航规划器基于对卡尔曼滤波器估计的定位不确定性的评估，计算合适的时机和偏航角指令，从而管理这一矛盾。仿真验证了所提算法的可扩展性。在真实世界实验中，我们将定位漂移降低了最多65%，并在室内和室外GPS拒止飞行中成功维持了给定编队，由此验证了所提系统的准确性、效率和鲁棒性。
英文摘要：
Relative state estimation is crucial for vision-based swarms to estimate and compensate for the unavoidable drift of visual odometry. For autonomous drones equipped with the most compact sensor setting -- a stereo camera that provides a limited field of view (FoV), the demand for mutual observation for relative state estimation conflicts with the demand for environment observation. To balance the two demands for FoV limited swarms by acquiring mutual observations with a safety guarantee, this paper proposes an active localization correction system, which plans camera orientations via a yaw planner during the flight. The yaw planner manages the contradiction by calculating suitable timing and yaw angle commands based on the evaluation of localization uncertainty estimated by the Kalman Filter. Simulation validates the scalability of our algorithm. In real-world experiments, we reduce positioning drift by up to 65% and managed to maintain a given formation in both indoor and outdoor GPS-denied flight, from which the accuracy, efficiency, and robustness of the proposed system are verified.
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。
