Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration
- URL: http://arxiv.org/abs/2304.14520v2
- Date: Wed, 21 Jun 2023 09:56:57 GMT
- Title: Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration
- Authors: Alexander Kyuroson, Niklas Dahlquist, Nikolaos Stathoulopoulos,
Vignesh Kottayam Viswanathan, Anton Koval and George Nikolakopoulos
- Abstract summary: This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
- Score: 55.41644538483948
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Algorithms for autonomous navigation in environments without Global
Navigation Satellite System (GNSS) coverage mainly rely on onboard perception
systems. These systems commonly incorporate sensors like cameras and Light
Detection and Rangings (LiDARs), the performance of which may degrade in the
presence of aerosol particles. Thus, there is a need of fusing acquired data
from these sensors with data from Radio Detection and Rangings (RADARs) which
can penetrate through such particles. Overall, this will improve the
performance of localization and collision avoidance algorithms under such
environmental conditions. This paper introduces a multimodal dataset from the
harsh and unstructured underground environment with aerosol particles. A
detailed description of the onboard sensors and the environment, where the
dataset is collected are presented to enable full evaluation of acquired data.
Furthermore, the dataset contains synchronized raw data measurements from all
onboard sensors in Robot Operating System (ROS) format to facilitate the
evaluation of navigation, and localization algorithms in such environments. In
contrast to the existing datasets, the focus of this paper is not only to
capture both temporal and spatial data diversities but also to present the
impact of harsh conditions on captured data. Therefore, to validate the
dataset, a preliminary comparison of odometry from onboard LiDARs is presented.
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