We present MOTLEE, a distributed mobile multi-object tracking algorithm that enables a team of robots to collaboratively track moving objects in the presence of localization error. Existing approaches to distributed tracking make limiting assumptions regarding the relative spatial relationship of sensors, including assuming a static sensor network or that perfect localization is available. Instead, we develop an algorithm based on the Kalman-Consensus filter for distributed tracking that properly leverages localization uncertainty in collaborative tracking. Further, our method allows the team to maintain an accurate understanding of dynamic objects in the environment by realigning robot frames and incorporating frame alignment uncertainty into our object tracking formulation. We evaluate our method in hardware on a team of three mobile ground robots tracking four people. Compared to previous works that do not account for localization error, we show that MOTLEE is resilient to localization uncertainties, enabling accurate tracking in distributed, dynamic settings with mobile tracking sensors.

翻译：摘要：我们提出MOTLEE，一种分布式移动多目标跟踪算法，可使机器人团队在存在定位误差的情况下协作跟踪移动物体。现有分布式跟踪方法对传感器的相对空间关系施加了严格假设，包括假设静态传感器网络或具备完美定位能力。为此，我们基于卡尔曼-协同滤波开发了一种分布式跟踪算法，该算法能合理利用定位不确定性进行协同跟踪。此外，我们的方法通过重新对齐机器人坐标系并将坐标系对齐不确定性纳入目标跟踪模型，使团队能够持续准确感知环境中的动态物体。我们在由三台移动地面机器人跟踪四名行人的硬件平台上评估了该方法。与未考虑定位误差的现有工作相比，我们证明MOTLEE对定位不确定性具有鲁棒性，可在配备移动跟踪传感器的分布式动态场景中实现精确跟踪。