  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.
