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English(EN) Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments

新方法利用证据图精炼激光雷达-相机标定

研究人员开发了一种新的激光雷达和相机系统标定方法,特别适用于农业环境。该方法采用“支持图驱动”技术来识别对准确标定最关键的观测,过滤掉噪声或模糊的数据。通过聚合对齐观测的一致性,该方法突出了可靠的标定证据,提高了在KITTI等数据集上的准确性。 AI

影响 提高了自动驾驶系统的传感器融合精度,可能增强在农业和机器人领域的性能。

排序理由 该集群包含一篇详细介绍一种新传感器标定方法的学术论文。

在 arXiv cs.CV 阅读 →

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Rajitha de Silva, Grzegorz Cielniak ·

    Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments

    arXiv:2605.23580v1 Announce Type: new Abstract: Reliable multi-modal calibration requires identifying which observations truly constrain the extrinsic parameters and which ones mainly add noise or ambiguity. In this paper, we propose a support-map-driven approach to multi-modal c…

  2. arXiv cs.CV TIER_1 English(EN) · Grzegorz Cielniak ·

    Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments

    Reliable multi-modal calibration requires identifying which observations truly constrain the extrinsic parameters and which ones mainly add noise or ambiguity. In this paper, we propose a support-map-driven approach to multi-modal calibration that decouples four functional blocks…