With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the information provided by a variety of complimentary sensors, it is necessary to accurately calibrate them. We take advantage of recent advances in computer graphics and implicit volumetric scene representation to tackle the problem of multi-sensor spatial and temporal calibration. Thanks to a new formulation of the Neural Radiance Field (NeRF) optimization, we are able to jointly optimize calibration parameters along with scene representation based on radiometric and geometric measurements. Our method enables accurate and robust calibration from data captured in uncontrolled and unstructured urban environments, making our solution more scalable than existing calibration solutions. We demonstrate the accuracy and robustness of our method in urban scenes typically encountered in autonomous driving scenarios.

翻译：随着自动驾驶技术的进步以及激光雷达成本的下降，多模态传感器系统的应用日益广泛。然而，为充分利用多种互补传感器提供的信息，必须对其进行精确标定。本文利用计算机图形学与隐式体素场景表示的最新进展，解决多传感器空间与时间标定问题。通过提出一种新的神经辐射场（NeRF）优化框架，我们能够基于辐射测量与几何测量，联合优化标定参数与场景表示。该方法能够从非受控、非结构化的城市环境中采集的数据实现精确鲁棒的标定，相比现有标定方案更具可扩展性。我们以自动驾驶场景中典型的城市场景验证了该方法的精度与鲁棒性。