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Vision-based Mobile Robot's Environment Outdoor Perception

Published: 22 October 2019 Publication History

Abstract

Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area.

References

[1]
He K, Zhang X, Ren S, et.al (2014). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 37(9), 1904--1916.
[2]
Long J, Shelhamer E, and Darrell T (2015). Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431--3440.
[3]
Yang P, Zhao P, Gao X (2017). Robust Online Multi-Task Learning with Correlative and Personalized Structures[J]. IEEE Transactions on Knowledge and Data Engineering, 29(11): 2510--2521.
[4]
Chen Y, Chen Y, Wang X, et al (2014). Deep Learning Face Representation by Joint Identification Verification[C]. International Conference on Neural Information Processing Systems. MIT Press,1988--1996.
[5]
Simonyan K and Zisserman A (2014). Very Deep Convolutional Networks for Large-scale Image Recognition. CoRR, abs/1409.1556.
[6]
Ren M and Zemel R S (2016). End-to-End Instance Segmentation and Counting with Recurrent Attention. CoRR, abs/1605.09410.
[7]
Redmon J, Divvala S K, Girshick R B, and Farhadi A (2015). You Only Look Once: Unified, Real-Time Object Detection. CoRR, abs/1506.02640.
[8]
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, and LeCun Y (2013). Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks. CoRR, abs/1312.6229.
[9]
Ren S, He K, Girshick R B, and Sun J (2015). Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks. CoRR, abs/1506.01497.
[10]
Kingma D P and Ba J (2014). Adam: A Method for Stochastic Optimization. CoRR, abs/1412.6980.
[11]
Fritsch J, Kuehnl T, and Geiger A (2013). A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. In International Conference on Intelligent Transportation Systems (ITSC),1693--1700.
[12]
Geiger A, Lenz P, and Urtasun R (2012). Are We Ready for Autonomous Driving?The Kitti Vision Benchmark Suite. In Conference on Computer Vision and Pattern Recognition (CVPR), 3354--3361.
[13]
Ma W C, Wang S, Brubaker M A, Fidler S, and Urtasun R (2016). Find Your Way by Observing the Sun and Other Semantic Cues. arXiv preprint arXiv:1606.07415.

Cited By

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  • (2024)An Algorithmic Study of Transformer-Based Road Scene Segmentation in Autonomous DrivingWorld Electric Vehicle Journal10.3390/wevj1511051615:11(516)Online publication date: 8-Nov-2024
  • (2023)Applying 3D Object Detection from Self-Driving Cars to Mobile Robots: A Survey and Experiments2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)10.1109/ICARSC58346.2023.10129637(3-9)Online publication date: 26-Apr-2023
  • (2022)Multi-layer segmentation solution to filter the noise of optical flow vectors to assist robots in object recognition inside buildings2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME55909.2022.9988355(1-6)Online publication date: 16-Nov-2022
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  1. Vision-based Mobile Robot's Environment Outdoor Perception

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    Published In

    cover image ACM Other conferences
    CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering
    October 2019
    942 pages
    ISBN:9781450362948
    DOI:10.1145/3331453
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 22 October 2019

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    Author Tags

    1. Automatic obstacle avoidance
    2. Multi-task learning
    3. Semantic segmentation
    4. Target detection

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    Overall Acceptance Rate 368 of 770 submissions, 48%

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    Cited By

    View all
    • (2024)An Algorithmic Study of Transformer-Based Road Scene Segmentation in Autonomous DrivingWorld Electric Vehicle Journal10.3390/wevj1511051615:11(516)Online publication date: 8-Nov-2024
    • (2023)Applying 3D Object Detection from Self-Driving Cars to Mobile Robots: A Survey and Experiments2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)10.1109/ICARSC58346.2023.10129637(3-9)Online publication date: 26-Apr-2023
    • (2022)Multi-layer segmentation solution to filter the noise of optical flow vectors to assist robots in object recognition inside buildings2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME55909.2022.9988355(1-6)Online publication date: 16-Nov-2022
    • (2020)Passive vision road obstacle detection: a literature mappingInternational Journal of Computers and Applications10.1080/1206212X.2020.175887744:4(376-395)Online publication date: 4-May-2020

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