DST-Calib: 二重経路・自己教師あり・ターゲット不要のLiDAR-カメラ外部キャリブレーションネットワーク
DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network
LiDARとカメラの外部キャリブレーションを、専用ターゲットや高精度正解ラベルなしでオンライン実行できる自己教師ありネットワークを提案。両側データ拡張と差分マップ構築により、汎化性能と精度を向上させた。
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著者: Zhiwei Huang, Yanwei Fu, Yi Zhou, Xieyuanli Chen, Qijun Chen, Rui Fan
分類: cs.RO, cs.CV
原文アブストラクト
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. Built upon this augmentation strategy, we design a dual-path, self-supervised calibration framework that reduces the dependence on high-precision ground truth labels and supports fully adaptive online calibration. Furthermore, to improve cross-modal feature association, we replace the traditional dual-branch feature extraction design with a difference map construction process that explicitly correlates LiDAR and camera features. This not only enhances calibration accuracy but also reduces model complexity. Extensive experiments conducted on five public benchmark datasets, as well as our own recorded dataset, demonstrate that the proposed method significantly outperforms existing approaches in terms of generalizability.
