Autonomous vehicles (AVs) fuse data from multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in sensor fusion; although radar measurements are sparse in comparison to camera images, radar scans penetrate fog, rain, and snow. However, accurate sensor fusion depends upon knowledge of the spatial transform between the sensors and any temporal misalignment that exists in their measurement times. During the life cycle of an AV, these calibration parameters may change, so the ability to perform in-situ spatiotemporal calibration is essential to ensure reliable long-term operation. State-of-the-art 3D radar-camera spatiotemporal calibration algorithms require bespoke calibration targets that are not readily available in the field. In this paper, we describe an algorithm for targetless spatiotemporal calibration that does not require specialized infrastructure. Our approach leverages the ability of the radar unit to measure its own ego-velocity relative to a fixed, external reference frame. We analyze the identifiability of the spatiotemporal calibration problem and determine the motions necessary for calibration. Through a series of simulation studies, we characterize the sensitivity of our algorithm to measurement noise. Finally, we demonstrate accurate calibration for three real-world systems, including a handheld sensor rig and a vehicle-mounted sensor array. Our results show that we are able to match the performance of an existing, target-based method, while calibrating in arbitrary, infrastructure-free environments.

翻译：自主驾驶车辆在恶劣环境下运行时，通过融合来自多传感器及多种感知模态的数据以提升鲁棒性。雷达与相机是传感器融合中的常用组合：尽管雷达测量数据相比相机图像较为稀疏，但其扫描能穿透雾、雨和雪等环境干扰。然而，精确的传感器融合依赖于传感器之间的空间变换关系以及测量时间中的任何临时性偏差。在自主驾驶车辆的生命周期中，这些标定参数可能会发生变化，因此具备现场进行时空标定的能力对于确保长期可靠运行至关重要。当前最先进的3D雷达-相机时空标定算法需要定制化标定目标，这些目标在野外环境中难以即时获取。本文提出一种无靶标的时空标定算法，无需特殊基础设施。该方法利用雷达单元相对于固定外部参考系测量自身运动速度的能力。我们分析了时空标定问题的可辨识性，并确定了标定所需的运动模式。通过一系列仿真研究，我们表征了算法对测量噪声的敏感度。最后，我们在三个实际系统中验证了标定精度，包括手持传感器架和车载传感器阵列。结果表明，该算法在任意无基础设施环境中进行标定时，能达到与现有基于靶标方法相当的性能。