FACT：面向基于视觉的无人机群的快速主动坐标初始化
FACT: Fast and Active Coordinate Initialization for Vision-based Drone Swarms
AI总结：
本文提出FACT系统，通过旋转观测、SDP求解非凸旋转估计及匈牙利算法恢复相对平移，解决基于视觉无人机群在SWaP约束下的快速鲁棒坐标初始化问题。
摘要：
群体机器人在多个领域引发了显著的发展。尽管在群体机器人的各种应用中是必要的，但基于视觉的无人机群中快速且鲁棒的坐标初始化仍然难以实现。为此，本文提出一个完整系统，在具有尺寸、重量和功率（SWaP）约束的平台上恢复群体初始相对位姿。为克服视场（FoV）有限的问题，无人机原地旋转以获取观测。为处理匿名测量，我们构建非凸旋转估计问题，并将其转化为半定规划（SDP）问题，从而稳定获取全局最优值。随后，我们利用匈牙利算法恢复相对平移以及观测与无人机身份之间的对应关系。为安全获取完整观测，我们主动搜索位置并生成可行轨迹以避免碰撞。为验证系统实用性，我们在仅以立体相机和惯性测量单元（IMU）作为传感器的基于视觉的无人机群上开展实验。结果表明，该系统在机载计算资源有限的情况下，能够实时鲁棒地获取准确的相对位姿。源代码已发布。
英文摘要：
Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.
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。
