BEVCALIB：基於幾何引導鳥瞰圖表示的LiDAR-相機校準

摘要

精確的LiDAR-相機校準是實現自動駕駛與機器人系統中多模態感知融合的基礎。傳統校準方法需在受控環境下進行大量數據採集，且無法補償車輛/機器人運動過程中的變換變化。本文提出首個利用鳥瞰圖（BEV）特徵從原始數據中進行LiDAR-相機校準的模型，命名為BEVCALIB。為此，我們分別提取相機BEV特徵與LiDAR BEV特徵，並將其融合至共享的BEV特徵空間。為充分利用BEV特徵中的幾何信息，我們引入了一種新穎的特徵選擇器，用於在變換解碼器中篩選最關鍵的特徵，從而降低內存消耗並實現高效訓練。在KITTI、NuScenes及我們自建數據集上的廣泛評估表明，BEVCALIB樹立了新的技術標杆。在各種噪聲條件下，BEVCALIB在KITTI數據集上以（平移，旋轉）指標分別平均超越文獻中最佳基線（47.08%，82.32%），在NuScenes數據集上則分別為（78.17%，68.29%）。在開源領域，它將最佳可復現基線提升了一個數量級。我們的代碼與演示結果可於https://cisl.ucr.edu/BEVCalib獲取。

English

Accurate LiDAR-camera calibration is fundamental to fusing multi-modal perception in autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled environments and cannot compensate for the transformation changes during the vehicle/robot movement. In this paper, we propose the first model that uses bird's-eye view (BEV) features to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve this, we extract camera BEV features and LiDAR BEV features separately and fuse them into a shared BEV feature space. To fully utilize the geometric information from the BEV feature, we introduce a novel feature selector to filter the most important features in the transformation decoder, which reduces memory consumption and enables efficient training. Extensive evaluations on KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a new state of the art. Under various noise conditions, BEVCALIB outperforms the best baseline in the literature by an average of (47.08%, 82.32%) on KITTI dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation, rotation), respectively. In the open-source domain, it improves the best reproducible baseline by one order of magnitude. Our code and demo results are available at https://cisl.ucr.edu/BEVCalib.

BEVCALIB：基於幾何引導鳥瞰圖表示的LiDAR-相機校準

BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations

摘要

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