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Targetless LiDAR-Camera Extrinsic Calibration via Class-Agnostic Boundary Mask Alignment and SPSA-Based Optimization.

Han-You Jeong¹, Woo-Hyuk Son¹, Dong-Wook Shin²

¹Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

This study introduces a novel targetless LiDAR-camera extrinsic calibration method using class-agnostic boundary mask alignment. The approach achieves robust and accurate sensor alignment, outperforming existing methods in challenging real-world conditions.

Keywords:

LiDAR–camera extrinsic calibration class-agonistic segmentation image-plane projection simultaneous perturbation stochastic approximation (SPSA)targetless sensor calibration

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Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Targetless extrinsic calibration for LiDAR-camera systems is difficult due to poor cross-modal correspondences and initialization sensitivity.
Existing methods often struggle with robustness and efficiency in real-world scenarios.

Purpose of the Study:

To develop a targetless LiDAR-camera extrinsic calibration framework that overcomes current limitations.
To improve the accuracy and robustness of sensor alignment for autonomous systems.

Main Methods:

A novel framework utilizing class-agnostic boundary mask alignment in a shared image-plane representation.
Construction of consistent LiDAR-camera mask pairs via image-plane projections.
Robust pose initialization using bounded rotation-only global optimization.
Refinement of extrinsic parameters via computationally efficient stochastic gradient approximation.

Main Results:

Demonstrated superior accuracy-runtime trade-off on the KITTI benchmark compared to segmentation-based global optimization.
Confirmed stable cross-modal alignment under vibration and inter-modal timing jitter in real-world driving tests.

Conclusions:

The proposed class-agnostic boundary mask alignment offers a robust and efficient solution for targetless LiDAR-camera extrinsic calibration.
This method enhances the reliability of sensor fusion for autonomous driving applications.