With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multi-modal 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 implicit model 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和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，为了有效利用各类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和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。通过隐式模型优化的新方法，我们能够基于辐射度测量与几何测量，联合优化标定参数与场景表示。该方法可在非受控、非结构化的城市环境中实现精确且鲁棒的标定，相较于现有标定方案具有更强的可扩展性。我们在自动驾驶场景中典型的城市环境下验证了该方法的准确性与鲁棒性。