研究人员开发了一种新颖的两阶段联合多摄像头激光雷达外参标定框架。该方法首先使用CMRNext进行初始成对估计，然后使用多帧捆绑调整进行精炼。该方法通过考虑多摄像头设置中的几何耦合来确保系统级一致性，在域内和域外数据集上均优于独立的成对方法。 AI
影响 通过确保多个摄像头和激光雷达传感器之间的校准一致性，提高自动驾驶系统的传感器融合精度。
排序理由 该集群包含一篇详细介绍摄像头-激光雷达标定新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.31576v1 Announce Type: new Abstract: Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera platforms. As a result, per-camera estimates may be individually accurate yet inc…
Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera platforms. As a result, per-camera estimates may be individually accurate yet inconsistent at the system level. We present a two-…