ShotcreteDepth: 用于喷射混凝土施工环境中鲁棒机器人深度感知的双模态数据集

摘要

我们介绍ShotcreteDepth，这是一个来自建筑领域的双模态数据集，同时捕捉了主动喷射混凝土过程和通用建筑环境。该数据集包含在恶劣真实世界条件下获取的立体RGB图像和LiDAR点云，这些条件包括高浊度和低照度。此类条件会对传感器测量造成不利影响，导致观测量不完整且充满噪声，给自主应用中的感知系统带来重大挑战。除了数据集外，我们还发布了一款轻量级标注工具，用于高效标注LiDAR点云。ShotcreteDepth包含11,252个时间同步的数据样本，其中220个已标注用于评估目的。该数据集可支持立体匹配、深度补全和深度估计研究，能够紧密反映工业场景中的操作复杂性。项目仓库：https://github.com/dtu-pas/shotcrete-depth

English

We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprises stereo RGB imagery and LiDAR point clouds acquired under harsh real-world conditions, including high turbidity and poor illumination. Such conditions adversely affect sensor measurements, leading to incomplete and noisy observations that pose significant challenges for perception systems in autonomous applications. Alongside the dataset, we release a lightweight annotation tool designed for time-efficient labeling of LiDAR point clouds. ShotcreteDepth consists of 11,252 temporally synchronized data samples, of which 220 are annotated for evaluation purposes. The dataset supports research in stereo matching, depth completion, and depth estimation under conditions that closely reflect the operational complexities found in industrial settings. Project repository: https://github.com/dtu-pas/shotcrete-depth