UniCalib: 統一深度表現上の確率的フローによるターゲットレスLiDAR-カメラキャリブレーション
UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations
LiDARとカメラのキャリブレーションをモダリティ内の深度フロー推定問題として再定式化し、共有特徴エンコーダと信頼度マップで高精度なターゲットレス校正を実現した。
著者: Shu Han, Xubo Zhu, Ji Wu, Ximeng Cai, Wen Yang, Huai Yu, Gui-Song Xia
分類: cs.RO, cs.CV
原文アブストラクト
Precise LiDAR-camera calibration is crucial for integrating these two sensors into robotic systems to achieve robust perception. In applications like autonomous driving, online targetless calibration enables a prompt sensor misalignment correction from mechanical vibrations without extra targets. However, existing methods exhibit limitations in effectively extracting consistent features from LiDAR and camera data and fail to prioritize salient regions, compromising cross-modal alignment robustness. To address these issues, we propose DF-Calib, a LiDAR-camera calibration method that reformulates calibration as an intra-modality depth flow estimation problem. DF-Calib estimates a dense depth map from the camera image and completes the sparse LiDAR projected depth map, using a shared feature encoder to extract consistent depth-to-depth features, effectively bridging the 2D-3D cross-modal gap. Additionally, we introduce a reliability map to prioritize valid pixels and propose a perceptually weighted sparse flow loss to enhance depth flow estimation. Experimental results across multiple datasets validate its accuracy and generalization,with DF-Calib achieving a mean translation error of 0.635cm and rotation error of 0.045 degrees on the KITTI dataset.
