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 and 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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PRMU2022-6 |
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 |
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Millimeter-wave Radar |
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Depth estimation |
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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 |
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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 |
6 |
Date of Issue |
2022-05-05 (PRMU) |
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