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Lighthouse Positioning System: Dataset, Accuracy, and Precision for UAV Research

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arxiv 2104.11523 v1 pith:6LTV5E2T submitted 2021-04-23 cs.RO

classification cs.RO
keywords systemlighthousetrackingaccuracydatasetsoftwareanalyzeattractive
verification ladder T0 review T1 audit T2 compute T3 formal
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The Lighthouse system was originally developed as tracking system for virtual reality applications. Due to its affordable price, it has also found attractive use-cases in robotics in the past. However, existing works frequently rely on the centralized official tracking software, which make the solution less attractive for UAV swarms. In this work, we consider an open-source tracking software that can run onboard small Unmanned Aerial Vehicles (UAVs) in real-time and enable distributed swarming algorithms. We provide a dataset specifically for the use cases i) flight; and ii) as ground truth for other commonly-used distributed swarming localization systems such as ultra-wideband. We then use this dataset to analyze both accuracy and precision of the Lighthouse system in different use-cases. To our knowledge, we are the first to compare two different Lighthouse hardware versions with a motion capture system and the first to analyze the accuracy using tracking software that runs onboard a microcontroller.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 15 citations worldwide. Full citation record

  1. Emergent Heterogeneous Swarm Control Through Hebbian Learning

    cs.NE 2025-07 conditional novelty 6.0 of 10

    A single evolved Hebbian update rule, shared by all agents, produces heterogeneous neural controllers that outperform homogeneous evolution and MARL baselines in simulated swarm tasks and in a small real-robot test.

  2. Lifelong Localization in Dynamic Indoor Environments Combining Odometry with Sparse Distance Sampling

    cs.RO 2026-07 conditional novelty 4.0 of 10

    A lifelong indoor localization framework fuses odometry with sparse distance sampling and provably retains a pose close to ground truth, provided the dynamic environment is correctly characterized.

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