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This paper introduces an uncertainty-aware adaptive consensus weighting mechanism for Distributed Kalman--Consensus Filter (DKCF) to improve Multi-Object Tracking (MOT) in mobile robot networks with heterogeneous localization uncertainty. The method dynamically adjusts the influence of neighbor information based on the covariance of transmitted estimates, mitigating the impact of unreliable data during distributed fusion. Simulation results demonstrate a MOTA improvement of 0.09 for agents with localization drift, showing the effectiveness of adaptive weighting in protecting local estimates from inconsistent data.
By adaptively weighting neighbor information based on uncertainty, distributed multi-object tracking can achieve significantly better performance in mobile robot networks with heterogeneous localization quality.
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.
