SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
arxiv(2023)
摘要
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.
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