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Targetless LiDAR-camera extrinsic calibration via semantic distribution alignment.

Xi Chen1,2, Bingyu Sun1

  • 1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.

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|March 25, 2026
PubMed
Summary

This study presents a novel targetless method for LiDAR-camera extrinsic calibration, improving robotic perception and localization. The approach ensures stable, robust calibration even with initial large errors, crucial for long-term robotic operation.

Keywords:
Jensen–Shannon divergencedirectional observability weightingrobotic perceptionsemantic distribution alignmenttargetless LiDAR–camera calibration

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate extrinsic calibration is vital for LiDAR-camera fusion systems in robotics.
  • Long-term operation can cause calibration drift, making recalibration challenging.
  • Existing targetless methods struggle with non-convex objectives and outdoor robustness.

Purpose of the Study:

  • To develop a robust, targetless extrinsic calibration method for LiDAR-camera systems.
  • To address the limitations of current calibration techniques in challenging environments.
  • To enable reliable long-term calibration for robotic applications.

Main Methods:

  • Minimizing semantic distribution consistency risk on SE(3) for targetless calibration.
  • Aligning semantic probability distributions from LiDAR and camera data.
  • Utilizing an anchor-fixed pixel sampling measure for stable optimization.
  • Implementing a direction-aware weighting strategy for improved rotation estimation.
  • Employing Jensen-Shannon divergence to handle semantic class imbalance.

Main Results:

  • The proposed method reliably converges from substantial initial perturbations.
  • Stable extrinsic estimates were achieved in experiments.
  • Demonstrated robustness in challenging outdoor scenarios.

Conclusions:

  • The novel targetless method offers a promising solution for maintaining LiDAR-camera extrinsic calibration.
  • The approach enhances robustness and stability for real-world robotic systems.
  • Facilitates reliable robotic localization and perception through consistent calibration.