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精度运行稳定性至关重要。为了在统一坐标系中正确利用每个传感器提供的信息，这些传感器必须进行精确标定。本文利用神经辐射场（NeRF）将不同传感器模态表示于统一体积表征的能力，实现了鲁棒且精确的时空传感器标定。通过设计基于每个传感器可见场景区域的分割方法，我们仅利用重叠区域构建标定问题。该策略使标定过程更鲁棒、更精确，且不易失效。通过多个主流驾驶数据集对城市户外场景的验证表明，与现有方法相比，本方法在精度和鲁棒性方面均具有更优表现。