论文标题
基于视觉的相对检测和跟踪微型航空车队
Vision-based Relative Detection and Tracking for Teams of Micro Aerial Vehicles
论文作者
论文摘要
在本文中，我们使用单个摄像头和惯性测量单元（IMU）以及相应的感知共识问题（即，所有观察者的独特性和相同的ID）来解决基于视觉的检测和跟踪多个航空车的问题。我们设计了几种基于视觉的分散贝叶斯多跟踪滤波策略，以解决视觉探测器算法获得的传入的未分类测量与跟踪剂之间的关联。我们根据团队中代理的数量在不同的操作条件以及可扩展性中比较它们的准确性。该分析提供了有关给定任务最合适的设计选择的有用见解。我们进一步表明，提议的感知和推理管道包括深度神经网络（DNN），因为视觉目标检测器轻巧，并且能够以尺寸，重量和功率（交换）在板载限制的机器人同时运行控制和计划。实验结果表明，在各种具有挑战性的场景（例如重型遮挡）中，有效跟踪了多个无人机。
In this paper, we address the vision-based detection and tracking problems of multiple aerial vehicles using a single camera and Inertial Measurement Unit (IMU) as well as the corresponding perception consensus problem (i.e., uniqueness and identical IDs across all observing agents). We design several vision-based decentralized Bayesian multi-tracking filtering strategies to resolve the association between the incoming unsorted measurements obtained by a visual detector algorithm and the tracked agents. We compare their accuracy in different operating conditions as well as their scalability according to the number of agents in the team. This analysis provides useful insights about the most appropriate design choice for the given task. We further show that the proposed perception and inference pipeline which includes a Deep Neural Network (DNN) as visual target detector is lightweight and capable of concurrently running control and planning with Size, Weight, and Power (SWaP) constrained robots on-board. Experimental results show the effective tracking of multiple drones in various challenging scenarios such as heavy occlusions.
