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Targetless Radar-Camera Calibration via Trajectory Alignment.

Ozan Durmaz1, Hakan Cevikalp1

  • 1Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Meselik, Eskisehir 26040, Turkey.

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|December 31, 2025
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Summary
This summary is machine-generated.

This study introduces a targetless method for calibrating radar and camera sensors, crucial for autonomous navigation. It achieves accurate sensor alignment by tracking object trajectories, enabling reliable multi-modal perception without physical markers.

Keywords:
cameraradarsensor calibrationsynchronizationtrajectory alignment

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate extrinsic calibration between radar and camera sensors is vital for multi-modal perception in robotics and autonomous navigation.
  • Traditional calibration methods using artificial targets are often impractical in dynamic or large-scale environments.

Purpose of the Study:

  • To present a fully targetless calibration framework for estimating the rigid spatial transformation between radar and camera coordinate frames.
  • To enable practical, markerless multi-sensor calibration for real-world autonomous systems.

Main Methods:

  • Integrating YOLOv5-based 3D object localization with DBSCAN and RANSAC for radar data processing.
  • Employing a passive temporal synchronization technique using RMSE minimization to correct timestamp offsets.
  • Utilizing Kabsch and Umeyama algorithms for computing rigid transformation parameters.

Main Results:

  • Achieved sub-degree rotational accuracy and decimeter-level translational error (0.12-0.27 m).
  • Demonstrated robust alignment despite radar sparsity and measurement bias.
  • Validated generalization to unseen motion trajectories using a drone as a dynamic target.

Conclusions:

  • The proposed targetless framework offers a practical solution for radar-camera calibration.
  • The method is suitable for real-world autonomous systems requiring markerless multi-sensor calibration.
  • The findings contribute to advancing reliable multi-modal perception in robotics.