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Paper Abstract and Keywords
Presentation 2022-05-13 14:50
Addressing Uncertain Radar-measurement Directions by Supervision using LiDAR
Masaya Kotani, Norimichi Ukita (TTI) PRMU2022-6
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, research on multimodal deep learning-based object detection and depth estimation methods using millimeter-wave radar and RGB images has attracted much attention due to their robustness in adverse weather conditions such as heavy rain and fog. However, millimeter-wave radar has the uncertainty of not knowing where in the vertical field of view the reflection came from (i.e., the elevation angle). Millimeter wave radar data with an undetermined elevation angle cannot be projected onto the image plane. Existing methods project all millimeter-wave radar data onto the image plane by assuming that they are reflected from the optical axis. However, this has the problem of projecting data reflected off the optical axis at the wrong position on the image plane. Existing methods exist to avoid projection, but no method exists to predict the elevation angle direction. In this study, we propose a method to predict the direction of elevation angle in millimeter-wave radar by supervised use of LiDAR data. The proposed method provides better depth estimation results than existing methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Millimeter-wave Radar / Depth estimation / LiDAR / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 13, PRMU2022-6, pp. 30-35, May 2022.
Paper # PRMU2022-6 
Date of Issue 2022-05-05 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2022-05-12 - 2022-05-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Toyota Technological Institute 
Topics (in Japanese) (See Japanese page) 
Topics (in English) How to conduct research (post-graduation project for students) 
Paper Information
Registration To PRMU 
Conference Code 2022-05-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Addressing Uncertain Radar-measurement Directions by Supervision using LiDAR 
Sub Title (in English)  
Keyword(1) Millimeter-wave Radar  
Keyword(2) Depth estimation  
Keyword(3) LiDAR  
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1st Author's Name Masaya Kotani  
1st Author's Affiliation Toyota Technological Institute (TTI)
2nd Author's Name Norimichi Ukita  
2nd Author's Affiliation Toyota Technological Institute (TTI)
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Speaker Author-1 
Date Time 2022-05-13 14:50:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2022-6 
Volume (vol) vol.122 
Number (no) no.13 
Page pp.30-35 
#Pages
Date of Issue 2022-05-05 (PRMU) 


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