Indoor obstacle detection system based on monocular vision and ultrasound applied to an intelligent car

TANG Ming; LI Yuanyuan

doi:10.12299/jsues.21-0136

Volume 35 Issue 1

Sep. 2022

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Article Navigation > Journal of Shanghai University of Engineering Science > 2022 > 36(1): 69-76

TANG Ming, LI Yuanyuan. Indoor obstacle detection system based on monocular vision and ultrasound applied to an intelligent car[J]. Journal of Shanghai University of Engineering Science, 2022, 36(1): 69-76. doi: 10.12299/jsues.21-0136

Citation:

TANG Ming, LI Yuanyuan. Indoor obstacle detection system based on monocular vision and ultrasound applied to an intelligent car[J]. Journal of Shanghai University of Engineering Science, 2022, 36(1): 69-76. doi: 10.12299/jsues.21-0136

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Indoor obstacle detection system based on monocular vision and ultrasound applied to an intelligent car

doi: 10.12299/jsues.21-0136

TANG Ming,
LI Yuanyuan^,

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2021-07-08
Publish Date: 2022-09-26

Abstract

Abstract

A method based on monocular vision and ultrasonic ranging for the intelligent Raspberry Pi robot for detecting static and dynamic obstacles was proposed. An improved monocular visual obstacle detection algorithm was applied to perform contour detection on indoor static and dynamic obstacles, the distance was measured between the robot car and obstacles with an ultrasonic sensor. For static obstacle detection, image enhancement was introduced in the image preprocessing stage, and different obstacle color features were extracted through HSV images to improve the efficiency and accuracy of obstacle contour calibration. For dynamic obstacle detection, background difference was combined with 3D image display technology to achieve dynamic target capture, and a distance decision module was set up to record obstacle location information. The experimental results show that the method can effectively reduce the average consumption time of obstacle detection, and improve the accuracy of indoor obstacle detection.
- monocular vision,
- ultrasonic sensor,
- indoor obstacle detection,
- image enhancement,
- background differencing

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References(22)

References

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