The advancement of computer vision and machine learning has made datasets a crucial element for further research and applications. However, the creation and development of robots with advanced recognition capabilities are hindered by the lack of appropriate datasets. Existing image or video processing datasets are unable to accurately depict observations from a moving robot, and they do not contain the kinematics information necessary for robotic tasks. Synthetic data, on the other hand, are cost-effective to create and offer greater flexibility for adapting to various applications. Hence, they are widely utilized in both research and industry. In this paper, we propose the dataset HabitatDyn, which contains both synthetic RGB videos, semantic labels, and depth information, as well as kinetics information. HabitatDyn was created from the perspective of a mobile robot with a moving camera, and contains 30 scenes featuring six different types of moving objects with varying velocities. To demonstrate the usability of our dataset, two existing algorithms are used for evaluation and an approach to estimate the distance between the object and camera is implemented based on these segmentation methods and evaluated through the dataset. With the availability of this dataset, we aspire to foster further advancements in the field of mobile robotics, leading to more capable and intelligent robots that can navigate and interact with their environments more effectively. The code is publicly available at https://github.com/ignc-research/HabitatDyn.

翻译：计算机视觉与机器学习的发展使得数据集成为推动进一步研究和应用的关键要素。然而，具备先进识别能力的机器人的创建与开发因缺乏合适的数据集而受阻。现有的图像或视频处理数据集无法准确描绘移动机器人的观测结果，且不包含机器人任务所需的运动学信息。相比之下，合成数据创建成本低廉，且更易于适应不同应用场景，因此在研究和工业领域被广泛使用。本文提出了HabitatDyn数据集，该数据集包含合成RGB视频、语义标签、深度信息以及运动学信息。HabitatDyn从搭载移动摄像头的移动机器人视角创建，涵盖30个场景，包含六种不同运动速度的移动物体。为验证数据集的实用性，我们采用两种现有算法进行评估，并基于这些分割方法实现了一种估算物体与相机之间距离的方法，通过数据集进行了验证。借助该数据集，我们期望推动移动机器人领域的进一步发展，从而打造出更强大、更智能的机器人，使其能够更有效地在环境中导航并与之交互。代码已在https://github.com/ignc-research/HabitatDyn公开。