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，使用具有不同模态的多传感器对于确保高操作精度与稳定性至关重要。为在单一公共坐标系中正确利用每个传感器提供的感知信息，这些传感器必须被精确标定。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。通过根据每个传感器的场景可见部分设计分区方法，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，且不易失败。我们通过在多个主流驾驶数据集上的验证，证明了该方法适用于户外城市场景。结果表明，与现有方法相比，我们的方法在精度与鲁棒性方面均更具优势。