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 assume either a static sensor network or that perfect localization is available. Instead, we develop algorithms based on the Kalman-Consensus filter for distributed tracking that are uncertainty-aware and properly leverage localization uncertainty. Our method maintains an accurate understanding of dynamic objects in an environment by realigning robot frames and incorporating uncertainty of frame misalignment 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.

翻译：我们提出MOTLEE，一种分布式移动多目标跟踪算法，使机器人团队能够在存在定位误差的情况下协作跟踪移动目标。现有分布式跟踪方法假设传感器网络是静态的或可实现完美定位。相反，我们基于卡尔曼-一致性滤波器开发了用于分布式跟踪的算法，该算法具有不确定性感知能力，并合理利用定位不确定性。我们的方法通过重新对齐机器人坐标系并将坐标系错位不确定性纳入目标跟踪公式中，保持对环境中动态对象的准确理解。我们在由三台移动地面机器人组成的硬件平台上评估了该方法，该平台用于跟踪四人。与未考虑定位误差的以往工作相比，我们证明MOTLEE对定位不确定性具有鲁棒性。