Abstract
Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and compute correspondences to optimize over. These approaches are costly and require substantial infrastructure and operations, making it challenging to scale for vehicle fleets. In this work, we propose UniCal, a unified framework for effortlessly calibrating SDVs equipped with multiple LiDARs and cameras. Our approach is built upon a differentiable scene representation capable of rendering multi-view geometrically and photometrically consistent sensor observations. We jointly learn the sensor calibration and the underlying scene representation through differentiable volume rendering, utilizing outdoor sensor data without the need for specific calibration fiducials. This "drive-and-calibrate" approach significantly reduces costs and operational overhead compared to existing calibration systems, enabling efficient calibration for large SDV fleets at scale. To ensure geometric consistency across observations from different sensors, we introduce a novel surface alignment loss that combines feature-based registration with neural rendering. Comprehensive evaluations on multiple datasets demonstrate that UniCal outperforms or matches the accuracy of existing calibration approaches while being more efficient, demonstrating the value of UniCal for scalable calibration.
Abstract (translated)
自动驾驶车辆（SDVs）需要准确校准激光雷达（LiDAR）和摄像机，以便在自主过程中融合传感器数据。传统的校准方法通常利用在受控且结构化的场景中捕获的 fiducials 来计算对应关系以优化。这些方法代价昂贵，需要大量的基础设施和操作，使得在车辆车队上扩展具有挑战性。在这项工作中，我们提出了 UniCal，一个用于轻松校准配备多个 LiDAR 和摄像机的 SDV 的统一框架。我们的方法基于具有渲染多视图几何和 photometric 一致性的可区分场景表示。我们通过不同的可区分体积渲染共同学习传感器校准和底层场景表示，利用户外传感器数据，无需特定校准 fiducials。这种“驾驶和校准”方法比现有校准系统显著减少了成本和操作开销，从而使大型 SDV 车队在规模上实现高效的校准。为了确保来自不同传感器的观察结果之间保持几何一致性，我们引入了一种新颖的表面对齐损失，将基于特征的注册与神经渲染相结合。在多个数据集上的全面评估表明，UniCal 优于或与现有校准方法的精度相等，同时更加高效，证明了 UniCal 在可扩展校准中的价值。
URL
https://arxiv.org/abs/2409.18953
