In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To correctly exploit the provided information by each sensor in a single common frame, it is essential for these sensors to be accurately calibrated. In this paper, we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration. By designing a partitioning approach based on the visible part of the scene for each sensor, we formulate the calibration problem using only the overlapping areas. This strategy results in a more robust and accurate calibration that is less prone to failure. We demonstrate that our approach works on outdoor urban scenes by validating it on multiple established driving datasets. Results show that our method is able to get better accuracy and robustness compared to existing methods.

翻译：在自动驾驶等快速发展的领域中，使用多模态传感器对于确保高操作精度和稳定性至关重要。为了在单一公共坐标系下正确利用各传感器提供的信息，这些传感器必须进行精确标定。本文利用神经辐射场将不同传感器模态表示为统一体素表征的能力，实现了鲁棒且精确的时空传感器标定。我们设计了一种基于各传感器场景可见部分的分区方法，仅利用重叠区域构建标定问题。该策略使标定更加鲁棒和精确，且不易失败。通过在多个成熟驾驶数据集上的验证，我们证明了该方法适用于城市场景。结果表明，与现有方法相比，我们的方法能够获得更高的精度和鲁棒性。