IEEE Transactions on Intelligent Transportation Systems, volume 23, issue 9, pages 1-11
Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images
Publication type: Journal Article
Publication date: 2022-09-01
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 8.5
ISSN: 15249050, 15580016
Computer Science Applications
Mechanical Engineering
Automotive Engineering
Abstract
The detection of dynamic and static obstacles is a key task for the navigation of autonomous ground vehicles. The article presents a new algorithm for generating an occupancy map of the surrounding space from noisy point clouds obtained from one or several stereo cameras. The camera images are segmented by the proposed deep neural network FCN-ResNet-M-OC, which combines the speed of the FCN-ResNet method and improves the quality of the model using the concept of object context representation. The paper investigates supervised approaches to network training on unbalanced samples with road scenes such as the weighted cross entropy and the Focal Loss. The occupancy map is built from point clouds with semantic labels, in which static environment and potentially dynamic obstacles are highlighted. Our solution is operational in real time and applicable on platforms with limited computing resources. The approach was tested on autonomous vehicle datasets: Semantic KITTI, KITTI-360, Mapillary Vistas and custom OpenTaganrog. The usage of semantically labeled point clouds increased the precision of obstacle detection by an average of 17%. The performance of the entire approach on various computing platforms with Jetson Xavier, RTX3070, GPUs NVidia Tesla V100 is respectively from 10 to 15 FPS for input image resolution
$1920\times 1080$
pixels.
Citations by journals
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Optical Memory and Neural Networks (Information Optics)
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Optical Memory and Neural Networks (Information Optics)
2 publications, 33.33%
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Lecture Notes in Computer Science
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Lecture Notes in Computer Science
1 publication, 16.67%
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IEEE Access
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IEEE Access
1 publication, 16.67%
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Robotics
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Robotics
1 publication, 16.67%
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Citations by publishers
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2
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Pleiades Publishing
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Pleiades Publishing
2 publications, 33.33%
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IEEE
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IEEE
2 publications, 33.33%
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Springer Nature
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Springer Nature
1 publication, 16.67%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 16.67%
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1
2
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Shepel I. et al. Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images // IEEE Transactions on Intelligent Transportation Systems. 2022. Vol. 23. No. 9. pp. 1-11.
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Shepel I., Adeshkin V., Belkin I., Yudin D. Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images // IEEE Transactions on Intelligent Transportation Systems. 2022. Vol. 23. No. 9. pp. 1-11.
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TY - JOUR
DO - 10.1109/TITS.2021.3133799
UR - https://doi.org/10.1109%2FTITS.2021.3133799
TI - Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images
T2 - IEEE Transactions on Intelligent Transportation Systems
AU - Shepel, Ilya
AU - Adeshkin, Vasily
AU - Belkin, Ilya
AU - Yudin, D.
PY - 2022
DA - 2022/09/01 00:00:00
PB - IEEE
SP - 1-11
IS - 9
VL - 23
SN - 1524-9050
SN - 1558-0016
ER -
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@article{2022_Shepel,
author = {Ilya Shepel and Vasily Adeshkin and Ilya Belkin and D. Yudin},
title = {Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images},
journal = {IEEE Transactions on Intelligent Transportation Systems},
year = {2022},
volume = {23},
publisher = {IEEE},
month = {sep},
url = {https://doi.org/10.1109%2FTITS.2021.3133799},
number = {9},
pages = {1--11},
doi = {10.1109/TITS.2021.3133799}
}
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MLA
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Shepel, Ilya, et al. “Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, Sep. 2022, pp. 1-11. https://doi.org/10.1109%2FTITS.2021.3133799.