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English(EN) Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions

单目SLAM系统在真实世界损坏下的鲁棒性评估

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的研究论文评估了单目SLAM系统的鲁棒性，特别是在各种真实世界损坏下，如恶劣天气和光照变化。该研究比较了一个经典的基于特征的系统和两个学习型跟踪器，分析它们的性能，不仅通过跟踪失败，还通过累积漂移。结果表明，学习型跟踪器倾向于表现出持续的漂移而不是灾难性的失败，并且它们的相对性能可能会根据用于测试的合成损坏的保真度而变化。 AI

影响 这项研究通过改进我们评估自主系统在挑战性环境条件下的性能的方式，可能带来更可靠的自主系统。

排序理由 关于评估计算机视觉算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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单目SLAM系统在真实世界损坏下的鲁棒性评估

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关于评估计算机视觉算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abhay Skaria Thomas, Shashank Agnihotri, Margret Keuper ·

    失败还是漂移？评估单目 SLAM 在合成和真实世界腐蚀下的表现

    arXiv:2608.30690v1 Announce Type: new Abstract: Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and sensor artifacts. Controlled corruptions are attractive because they isolate such fac…

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