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Related Experiment Video

Updated: Sep 29, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
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Automatic Extrinsic Calibration of 3D LIDAR and Multi-Cameras Based on Graph Optimization.

Jinshun Ou1,2, Panling Huang1,2, Jun Zhou1,2

  • 1School of Mechanical Engineering, Shandong University, Jinan 250061, China.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary

This study introduces an automatic 3D LIDAR-to-camera calibration framework using graph optimization. The method achieves high accuracy and robustness, outperforming existing techniques for sensor fusion applications.

Keywords:
3D LIDARautomatic extrinsic calibrationgraph optimizationmulti-camerasvirtual feature point

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Area of Science:

  • Robotics and Computer Vision
  • Sensor Fusion and Calibration

Background:

  • Extrinsic calibration is crucial for multi-sensor fusion in applications like autonomous driving and 3D reconstruction.
  • Existing calibration methods face challenges in accuracy and robustness.

Purpose of the Study:

  • To propose an automatic 3D LIDAR-to-camera calibration framework.
  • To simultaneously calibrate LIDAR and multiple cameras.
  • To improve calibration accuracy and robustness.

Main Methods:

  • A novel framework based on graph optimization for automatic LIDAR-to-camera calibration.
  • Automatic identification of calibration patterns and creation of virtual feature point clouds.
  • Simultaneous calibration of LIDAR with monocular and binocular cameras.

Main Results:

  • Achieved an average error of 0.161 mm on the camera normalization plane.
  • Demonstrated superior accuracy compared to state-of-the-art methods.
  • The graph optimization simultaneously refines point clouds, correcting data collection errors and enhancing robustness.

Conclusions:

  • The proposed graph optimization-based framework provides accurate and robust automatic calibration for LIDAR-camera systems.
  • This method is effective for multi-sensor fusion applications, including autonomous driving and 3D mapping.
  • The framework's ability to optimize point clouds makes it resilient to noisy data.