发表机构
KAIST (Korea Advanced Institute of Science and Technology); Kookmin University; LG Innotek（韩国科学技术院（KAIST）; 国民大学; LG伊诺特）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种联合优化框架，通过构建各传感器对残差并同时优化外参，结合自适应雷达噪声滤波和对应累积策略，实现在线无靶标雷达-激光雷达-相机标定，降低标定误差。
AI 中文摘要
融合雷达、激光雷达和相机能够在多样化和恶劣条件下实现稳健的感知，但融合性能关键取决于三种传感器之间的精确外参标定。本文针对雷达-激光雷达-相机系统，研究了在线无靶标外参标定问题。现有的无靶标方法大多针对单一传感器对设计，而将成对结果组合并不能保证三种传感器之间的一致性。此外，稀疏且含噪的雷达测量使得涉及雷达的传感器对不可靠。为应对这些挑战，我们提出了一种联合标定框架，为每个传感器对构建残差，并同时优化所有传感器对的外参以最小化总体残差。进一步地，我们引入了一种自适应雷达噪声滤波器，利用随距离变化的阈值拒绝虚假雷达回波，以及一种对应累积策略，跨帧聚合稀疏的雷达对应点。我们在覆盖多样城市环境的自建雷达-激光雷达-相机数据集上验证了该方法，与最先进的相机-激光雷达基线相比，该方法降低了所有传感器对的标定误差。
英文摘要
Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate extrinsic calibration among the three sensors. In this paper, we address the problem of online target-less extrinsic calibration for the radar-LiDAR-camera system. Existing target-less methods are mostly designed for a single sensor pair, and composing the pairwise results does not guarantee consistency across the three sensors. Moreover, the sparse and noisy radar measurements make the radar-involving pairs unreliable. To tackle these challenges, we propose a joint calibration framework that constructs residuals for each sensor pair and optimizes the extrinsics of all pairs together to minimize the overall residual. Furthermore, we introduce an adaptive radar noise filter that rejects spurious radar returns using a range-dependent margin, and a correspondence accumulation strategy that aggregates sparse radar correspondences over frames. We validate our method on an in-house radar-LiDAR-camera dataset covering diverse urban environments, where it reduces calibration errors across all sensor pairs over a state-of-the-art camera-LiDAR baseline.
Comments6 pages, 2 figures, 3 tables. Accepted to the International Conference on Control, Automation and Systems (ICCAS 2026)
