移動ロボットネットワークにおける適応的不確実性重み付けを用いた分散カルマン・コンセンサスフィルタによる複数物体追跡
Distributed Kalman--Consensus Filtering with Adaptive Uncertainty Weighting for Multi-Object Tracking in Mobile Robot Networks
移動ロボットネットワークでの複数物体追跡において、各ロボットの位置推定精度の違いによるフレームずれを補正しつつ、推定の共分散に基づいて隣接情報の影響を動的に調整する適応的重み付けを提案し、追跡性能を向上させた。
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著者: Niusha Khosravi, Rodrigo Ventura, Meysam Basiri
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
