LATIDIA · Robótica

SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable Synthesis

arXiv: 2610.27547v1Tipo de anuncio: Cross Resumen: La calibración de cámaras LiDAR en línea sigue siendo una tarea fundamental pero desafiante en el campo de la robótica. Recientemente, han surgido enfoques basados en datos basados en el aprendizaje profundo, de

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arXiv:2610.27547v1 Announce Type: cross Abstract: Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approaches have emerged, offering high-precision, targetless calibration capabilities. However, existing methods often suffer from a critical dataset shortcut phenomenon due to the lack of extrinsic diversity in their training data. Networks tend to overfit to fixed nominal extrinsic configurations, leading to severe performance degradation and poor generalization in real-world deployments. To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks. Our framework leverages existing omnidirectional image datasets annotated with depth ground truth to synthesize multi-modal calibration data under rigorous