New CELL method enhances event-camera localization in LiDAR maps

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种名为CELL的新方法，用于改进事件相机在LiDAR地图中的定位。该技术学会为事件图像和LiDAR深度视图之间的对应关系分配置信度分数，解决了深度较大处的对应关系对姿态估计信息量较少的偏差。通过使用可微分的概率性PnP求解器，CELL能够端到端地学习这些置信度，然后在训练期间用于重新加权监督，并在测试时优化姿态估计。CELL系统在M3ED和DSEC数据集上展示了改进，与LEAR基线相比，中值平移误差最多降低了26.9%，中值旋转误差最多降低了15.8%。 AI

影响提高了结合事件相机和LiDAR的系统的定位精度，可能有利于自主导航和机器人技术。

排序理由该集群包含一篇详细介绍新计算机视觉方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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New CELL method enhances event-camera localization in LiDAR maps

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arXiv cs.CV TIER_1 English(EN) · Panagiotis Kiousis, Kuangyi Chen, Jun Zhang, Friedrich Fraundorfer · 2026-10-09 04:00

学习信任哪些对应关系：激光雷达地图中的置信度加权事件相机定位

arXiv:2610.11967v1 Announce Type: new Abstract: Localizing an event camera against a pre-built LiDAR map can be cast as dense optical-flow estimation between a rendered depth view and an event image, followed by a Perspective-n-Point (PnP) solver over the induced 3D-2D correspond…

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学习信任哪些对应关系：激光雷达地图中的置信度加权事件相机定位

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