  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.
