Abstract
This paper presents an implementation and evaluation of a Distributed Kalman--Consensus Filter (DKCF) for Multi-Object Tracking (MOT) in mobile robot networks operating under partial observability and heterogeneous localization uncertainty. A key challenge in such systems is the fusion of information from agents with differing localization quality, where frame misalignment can lead to inconsistent estimates, track duplication, and ghost tracks. To address this issue, we build upon the MOTLEE framework and retain its frame-alignment methodology, which uses consistently tracked dynamic objects as transient landmarks to improve relative pose estimates between robots. On top of this framework, we propose an uncertainty-aware adaptive consensus weighting mechanism that dynamically adjusts the influence of neighbor information based on the covariance of the transmitted estimates, thereby reducing the impact of unreliable data during distributed fusion. Local tracking is performed using a Kalman Filter (KF) with a Constant Velocity Model (CVM) and Global Nearest Neighbor (GNN) data association. simulation results demonstrate that adaptive weighting effectively protects local estimates from inconsistent data, yielding a MOTA improvement of 0.09 for agents suffering from localization drift, although system performance remains constrained by communication latency.
Abstract (translated)
本文提出了一种分布式卡尔曼-共识滤波器（DKCF）的实现和评估，用于在部分可观察性和异构定位不确定性条件下运行的移动机器人网络中的多目标跟踪（MOT）。此类系统的一个关键挑战是从具有不同定位质量的代理中融合信息，这可能导致帧对齐错误、估计不一致、重复跟踪以及虚警。为了解决这一问题，我们基于MOTLEE框架构建，并保留其帧对齐方法，该方法利用持续跟踪的动力对象作为瞬时地标以改进机器人之间的相对姿态估计。在此基础上，我们提出了一种感知不确定性的自适应共识加权机制，通过根据传输估算的协方差动态调整邻居信息的影响来减少分布式融合期间不可靠数据的影响。本地跟踪使用恒定速度模型（CVM）和全局最近邻（GNN）数据关联的卡尔曼滤波器（KF）进行。 仿真结果表明，自适应加权有效保护了局部估计免受不一致数据的影响，在遭受定位漂移的代理中MOTA指标提高了0.09，尽管系统性能仍受到通信延迟的限制。
URL
https://arxiv.org/abs/2603.11328
