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FlowCalib: Targetless Infrastructure LiDAR-Camera Extrinsic Calibration Based on Optical Flow and Scene Flow

Renwei Hai,Yanqing Shen,Yuchen Yan,Shitao Chen,Jingmin Xin,Nanning Zheng

Published 2026 in IEEE transactions on intelligent transportation systems (Print)

ABSTRACT

Recently, multi-sensor fusion-based vehicle infrastructure cooperative perception has aroused extensive attention due to the demands for the safety of autonomous driving and traffic monitoring. An accurate calibration between different sensors is a critical foundation for most sensor fusion systems. For LiDAR-camera calibration, high accuracy can be achieved with the help of artificial calibration targets, such as a checkerboard. However, unlike autonomous vehicles, roadside sensors monitor traffic scenes with continuous traffic flow from a fixed viewpoint, posing challenges for conventional calibration methods. There, a calibration method suitable for roadside scenes is required for infrastructure sensors. In this paper, we propose FlowCalib, a novel targetless infrastructure LiDAR-camera spatial calibration method through alignment of scene flow and optical flow. The main idea is to leverage the inherent consistency of moving objects in traffic flow across two types of sensor data. Firstly, the moving objects are extracted by optical flow and scene flow. Then, the extrinsic parameters are obtained in two steps: rough calibration and calibration refinement. In rough calibration, the center and motion flow of each moving instance are calculated by clustering methods separately in the point cloud and image. Based on this, the possible initial value set of extrinsic parameters is estimated by two-step parameter sampling. The initial parameters are obtained by distance of center and motion flow in point cloud and image based scoring. Subsequently, the extrinsic parameters are refined by optimization of instance alignment loss and flow alignment loss of moving objects. In the end, quantitative and qualitative experiments are conducted to validate the effectiveness of the algorithm across both simulated datasets and real-world datasets.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE transactions on intelligent transportation systems (Print)

  • Publication date

    2026-01-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TITS.2025.3627651
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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