Autonomous vehicles (AVs) often depend on 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 combination; although radar measurements are sparse in comparison to camera images, radar scans are able to penetrate fog, rain, and snow. Data from both sensors are typically fused prior to use in downstream perception tasks. 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 \emph{targetless} spatiotemporal calibration that is able to operate without 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：自动驾驶汽车（AVs）在恶劣环境中运行时，通常依赖多种传感器和感知方式以提升鲁棒性。雷达和相机是常用的组合方案：尽管相较于相机图像，雷达测量数据较为稀疏，但其能够穿透雾、雨和雪。来自两种传感器的数据通常先融合，再用于下游感知任务。然而，准确的传感器融合依赖于对传感器间空间变换关系以及测量时间时序偏差的精确掌握。在自动驾驶汽车的生命周期中，这些标定参数可能发生变化，因此具备原位时空标定能力对于确保长期稳定运行至关重要。现有最先进的3D雷达-相机时空标定算法需要定制化标定靶标，而这些靶标在实际场景中难以获取。本文提出了一种无需靶标的时空标定算法，可在无专用基础设施的条件下运行。该方法利用雷达单元测量自身相对于固定外部参考系的自速度能力。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灵敏度。最后，我们在三个实际系统（包括手持传感器平台和车载传感器阵列）上验证了标定精度。结果表明，本方法在任意无基础设施环境中实现标定时，其性能可与现有的基于靶标的方法相媲美。