Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigid-body transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors - however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments.

翻译：在恶劣天气中可靠运行对于部署安全的自主飞行器至关重要。通过将标准的AV传感器套件(即激光雷达、照相机)的数据与毫米波长雷达等天气强力传感器(如毫米波长雷达)混合起来,可以实现强健和可靠性。精确的传感器数据聚合需要了解感应对对等传感器之间的硬体变异,这种变异可以通过外部校准过程确定。为2D(平面)雷达传感器设计了一些外部校准算法,然而,最近开发的低成本3D毫米波长雷达可以在许多应用中取代对等的2D。在本论文中,我们提出了一个连续的3D雷达到磁带校准算法,利用雷达速度测量,与大多数现有技术不同的是,并不需要专门的雷达反射器存在于环境中。我们通过合成和现实世界实验获得我们的配方的可耐性特征,并展示我们的算法的功效。