Publication
Title
Predicting LiDAR data from sonar images
Author
Abstract
Sensors using ultrasonic sound have proven to provide accurate 3D perception in difficult environments where other modalities fail. Several industrial sectors need accurate and reliable sensing in these harsh conditions. The conventional LiDAR/camera approach in many state-of-the-art autonomous navigation methods is limited to environments with optimal sensing conditions for visual modalities. The use of other sensing modalities can thus improve reliability and usability and increase the application potential of autonomous agents. Ultrasonic measurements provide, compared to LiDAR, a much sparser representation of the environment, making a direct replacement of the LiDAR sensor difficult. In this work, we propose a method to predict LiDAR point cloud data from an in-air acoustic sonar sensor using a convolutional stacked autoencoder. This provides a robotic system with high-resolution measurements and allows for easier integration into existing systems to safely navigate environments where visual modalities become unreliable and less accurate. A video of our predictions is available at https://youtu.be/jlx1S-tslmo.
Language
English
Source (journal)
IEEE access. - New York, N.Y., 2013, currens
Publication
New York, N.Y. : IEEE , 2021
ISSN
2169-3536
DOI
10.1109/ACCESS.2021.3072551
Volume/pages
9 (2021) , p. 57897-57906
ISI
000641952200001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 05.05.2021
Last edited 02.10.2024
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