Publication
Title
R2L-SLAM : sensor fusion-driven SLAM using mmWave Radar, LiDAR and deep neural networks
Author
Abstract
Optical sensing modalities are extensively used in autonomous vehicles (AVs). These sensors are, however, not always reliable, particularly in harsh or difficult sensing conditions, such as with smoke or rain. This limitation can impact their application potential due to safety concerns, since optical sensors can fail to reliably perceive obstacles in such harsh conditions. To address this, it would be desirable to include other modalities, such as radar, into the perception sensor suites of these AVs. However, this is difficult because many recent state-of-the-art navigation algorithms are designed specifically for LiDAR sensors. In this work, we propose a modality prediction method that allows for the addition of a single-chip mmWave radar sensor to an existing sensor setup consisting of a 2D LiDAR sensor, without changing the current downstream applications. We demonstrate the increased reliability of our method in situations where optical sensing modalities become less accurate and unreliable.
Language
English
Source (journal)
Proceedings of IEEE Sensors. - Piscataway, NJ, 2002, currens
Source (book)
2023 IEEE SENSORS, 29 October - 01 November, 2023, Vienna, Austria
Publication
Piscataway, NJ : IEEE , 2023
ISSN
1930-0395
ISBN
979-83-503-0387-2
DOI
10.1109/SENSORS56945.2023.10324990
Volume/pages
p. 1-4
ISI
001116741300142
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
Goldilocks' Fusion: Adaptive and Robust Sensor Fusion in Resource-Constrained Robotic Systems.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 05.12.2023
Last edited 29.05.2024
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