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ARXIV:2603.11328 · MULTI-OBJECT TRACKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11328MULTI-OBJECT TRACKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting.
Opportunity summary
Pain A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting. A key challenge in such systems is the fusion of information from agents with differing 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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting.
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10.48550/arXiv.2603.11328A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting.
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.
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unverified0 refs; 0 sources; 17% coverage.
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PROBLEM
A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting. A key challenge in such systems is the fusion of information from agents with differing localization quality, where frame misalignment can l...
METHOD
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...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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 estima...
WHY NOW
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A distributed Kalman filter framework for improving multi-object tracking in mobile robot networks through adaptive uncertainty weighting.
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Multi-Object Tracking
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Commercial read
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