DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network
Zhiwei Huang, Yanwei Fu, Yi Zhou, Xieyuanli Chen, Qijun Chen, Rui Fan

TL;DR
DST-Calib introduces a novel self-supervised, target-free LiDAR-camera calibration method that uses double-sided data augmentation and a dual-path framework to improve robustness, accuracy, and online adaptability in diverse environments.
Contribution
It is the first to propose a self-supervised, target-free calibration network with double-sided augmentation and a difference map for cross-modal feature correlation.
Findings
Outperforms existing methods in generalizability across five benchmark datasets.
Operates in an online, fully adaptive manner without calibration targets.
Reduces model complexity while improving calibration accuracy.
Abstract
LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration targets (e.g., checkerboards) or specific, static scene types, limiting their adaptability and deployment in real-world autonomous and robotic applications. This article presents the first self-supervised LiDAR-camera extrinsic calibration network that operates in an online fashion and eliminates the need for specific calibration targets. We first identify a significant generalization degradation problem in prior methods, caused by the conventional single-sided data augmentation strategy. To overcome this limitation, we propose a novel double-sided data augmentation technique that generates multi-perspective camera views using estimated depth maps, thereby enhancing robustness and diversity during training.…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Optical measurement and interference techniques
