Abstract
In recent years, researches on autonomous mobile robots are being advanced, and among them, practical use of automatic driving cars is expected particularly. In this realization, there is a parking problem as one of the problems to be solved. Currently, although various sensors such as infrared lasers and millimeter wave radars are used for parking problems, it is desirable to use the less number of sensors and select cheaper ones when considering the cost. This research aims to recognize the surrounding environment for parking control by only images obtained from using a monocular camera. For safe parking, it needs to detect the parking position and check the space to turn the wheel. A technique called Convolutional Neural Network (CNN), which is now a mainstream in image recognition fields, is used to archive them. This paper constructs a system for judging a parking possibility using object detection and a system for classifying several patterns of the space to turn the wheel using depth images. Some simulations are conducted to verify whether the surrounding environment in parking can be integratedly judged by the proposed system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 833-838 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728116983 |
| DOIs | |
| Publication status | Published - Aug 1 2019 |
| Event | 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019 - Tianjin, China Duration: Aug 4 2019 → Aug 7 2019 |
Publication series
| Name | Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019 |
|---|
Conference
| Conference | 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019 |
|---|---|
| Country | China |
| City | Tianjin |
| Period | 8/4/19 → 8/7/19 |
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Keywords
- Autonomous Mobile Robot
- Convolutional Neural Network
- Paking Problem
ASJC Scopus subject areas
- Signal Processing
- Mechanical Engineering
- Control and Optimization
- Artificial Intelligence
Cite this
Proposal of an Environmental Recognition Method for Automatic Parking by an Image-based CNN. / Yamamoto, Kazuki; Watanabe, Keigo; Nagai, Isaku.
Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 833-838 8816556 (Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Proposal of an Environmental Recognition Method for Automatic Parking by an Image-based CNN
AU - Yamamoto, Kazuki
AU - Watanabe, Keigo
AU - Nagai, Isaku
PY - 2019/8/1
Y1 - 2019/8/1
N2 - In recent years, researches on autonomous mobile robots are being advanced, and among them, practical use of automatic driving cars is expected particularly. In this realization, there is a parking problem as one of the problems to be solved. Currently, although various sensors such as infrared lasers and millimeter wave radars are used for parking problems, it is desirable to use the less number of sensors and select cheaper ones when considering the cost. This research aims to recognize the surrounding environment for parking control by only images obtained from using a monocular camera. For safe parking, it needs to detect the parking position and check the space to turn the wheel. A technique called Convolutional Neural Network (CNN), which is now a mainstream in image recognition fields, is used to archive them. This paper constructs a system for judging a parking possibility using object detection and a system for classifying several patterns of the space to turn the wheel using depth images. Some simulations are conducted to verify whether the surrounding environment in parking can be integratedly judged by the proposed system.
AB - In recent years, researches on autonomous mobile robots are being advanced, and among them, practical use of automatic driving cars is expected particularly. In this realization, there is a parking problem as one of the problems to be solved. Currently, although various sensors such as infrared lasers and millimeter wave radars are used for parking problems, it is desirable to use the less number of sensors and select cheaper ones when considering the cost. This research aims to recognize the surrounding environment for parking control by only images obtained from using a monocular camera. For safe parking, it needs to detect the parking position and check the space to turn the wheel. A technique called Convolutional Neural Network (CNN), which is now a mainstream in image recognition fields, is used to archive them. This paper constructs a system for judging a parking possibility using object detection and a system for classifying several patterns of the space to turn the wheel using depth images. Some simulations are conducted to verify whether the surrounding environment in parking can be integratedly judged by the proposed system.
KW - Autonomous Mobile Robot
KW - Convolutional Neural Network
KW - Paking Problem
UR - http://www.scopus.com/inward/record.url?scp=85072393995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072393995&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816556
DO - 10.1109/ICMA.2019.8816556
M3 - Conference contribution
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 833
EP - 838
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -