Autonomous vehicles (AVs) often depend on multiple sensors and sensing modalities to mitigate data degradation and provide a measure of robustness when operating in adverse conditions. Radars and cameras are a popular sensor 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. 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 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：自动驾驶汽车通常依赖多传感器及多种感知模态，以缓解数据退化问题，并在恶劣环境下提供一定程度的鲁棒性。雷达与相机是常见的传感器组合——尽管雷达测量数据较相机图像稀疏，但雷达扫描能够穿透雾、雨和雪。来自两种传感器的数据通常在用于下游感知任务前进行融合。然而，精确的传感器融合依赖于对传感器间空间变换及其测量时间中存在的任何时间失配的认知。在自动驾驶汽车生命周期内，这些标定参数可能发生变化。具备现场时空标定能力对于确保长期可靠运行至关重要。现有最先进的三维雷达-相机时空标定算法需要专用的标定靶标，这些靶标在实际场景中难以获取。本文描述了一种无需专用靶标的时空标定算法，该算法无需专门基础设施即可运行。我们的方法利用雷达单元测量自身相对于固定外部参考系的速度的能力。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。通过一系列仿真研究，我们表征了算法对测量噪声的敏感性。最后，我们在三个实际系统（包括手持传感器支架和车载传感器阵列）上展示了精确标定效果。结果表明，我们能够在任意（无基础设施）环境中进行标定，同时达到与现有基于靶标方法相当的精度。