Estimating the 2D Static Map Based on Moving Stereo Camera

Shadi M. Saleh, Sinan A. Khwandah, Wolfram Hardt, Marcus Hilbrich, Pavlos I. Lazaridis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual information in any weather condition and they are expensive. Furthermore, they require intensive calculations. Therefore, they can't be easily processed online. The aim of this study is to provide a solution based on the low-cost, light, and low-power stereo camera. The proposed solution focuses on the spatial information about the driving environment which is represented as a 3D point cloud. These post-processed points are projected on the 2D rectangle grid and divided into identical square cells. Each cell is holding information about the 3D points that lie over it and this created what is called a height map. In the same time, a confidence map is built to reduce and discard scattered points because the produced 3D point cloud is noisy. Finally, an occlusion map is constructed to estimate the status of each cell as a border, free or occluded.

Original languageEnglish
Title of host publication24th IEEE International Conference on Automation and Computing (ICAC)
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781862203419, 9781862203426
ISBN (Print)9781538648919
DOIs
Publication statusPublished - 1 Jul 2019
Event24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Newcastle University, Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018
Conference number: 24
https://ieeexplore.ieee.org/xpl/conhome/8742895/proceeding (Website Containing the Proceedings)
http://www.cacsuk.co.uk/index.php/conferences/icac (Link to Conference Information)

Conference

Conference24th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2018
CountryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18
Internet address

Fingerprint

Camera
Cameras
Point Cloud
Cell
What is this
Intelligent Vehicle
Intelligent vehicle highway systems
Sensor
Autonomous Vehicles
Lidar
Sensors
Spatial Information
Occlusion
Weather
Rectangle
Radar
Confidence
Navigation
Safety
Grid

Cite this

Saleh, S. M., Khwandah, S. A., Hardt, W., Hilbrich, M., & Lazaridis, P. I. (2019). Estimating the 2D Static Map Based on Moving Stereo Camera. In X. Ma (Ed.), 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing [8749004] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8749004
Saleh, Shadi M. ; Khwandah, Sinan A. ; Hardt, Wolfram ; Hilbrich, Marcus ; Lazaridis, Pavlos I. / Estimating the 2D Static Map Based on Moving Stereo Camera. 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. editor / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019.
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Saleh, SM, Khwandah, SA, Hardt, W, Hilbrich, M & Lazaridis, PI 2019, Estimating the 2D Static Map Based on Moving Stereo Camera. in X Ma (ed.), 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing., 8749004, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Conference on Automation and Computing, Newcastle upon Tyne, United Kingdom, 6/09/18. https://doi.org/10.23919/IConAC.2018.8749004

Estimating the 2D Static Map Based on Moving Stereo Camera. / Saleh, Shadi M.; Khwandah, Sinan A.; Hardt, Wolfram; Hilbrich, Marcus; Lazaridis, Pavlos I.

24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. ed. / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019. 8749004.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Estimating the 2D Static Map Based on Moving Stereo Camera

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AU - Khwandah, Sinan A.

AU - Hardt, Wolfram

AU - Hilbrich, Marcus

AU - Lazaridis, Pavlos I.

PY - 2019/7/1

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N2 - Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual information in any weather condition and they are expensive. Furthermore, they require intensive calculations. Therefore, they can't be easily processed online. The aim of this study is to provide a solution based on the low-cost, light, and low-power stereo camera. The proposed solution focuses on the spatial information about the driving environment which is represented as a 3D point cloud. These post-processed points are projected on the 2D rectangle grid and divided into identical square cells. Each cell is holding information about the 3D points that lie over it and this created what is called a height map. In the same time, a confidence map is built to reduce and discard scattered points because the produced 3D point cloud is noisy. Finally, an occlusion map is constructed to estimate the status of each cell as a border, free or occluded.

AB - Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual information in any weather condition and they are expensive. Furthermore, they require intensive calculations. Therefore, they can't be easily processed online. The aim of this study is to provide a solution based on the low-cost, light, and low-power stereo camera. The proposed solution focuses on the spatial information about the driving environment which is represented as a 3D point cloud. These post-processed points are projected on the 2D rectangle grid and divided into identical square cells. Each cell is holding information about the 3D points that lie over it and this created what is called a height map. In the same time, a confidence map is built to reduce and discard scattered points because the produced 3D point cloud is noisy. Finally, an occlusion map is constructed to estimate the status of each cell as a border, free or occluded.

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KW - Height map

KW - Occlusion map

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DO - 10.23919/IConAC.2018.8749004

M3 - Conference contribution

SN - 9781538648919

BT - 24th IEEE International Conference on Automation and Computing (ICAC)

A2 - Ma, Xiandong

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Saleh SM, Khwandah SA, Hardt W, Hilbrich M, Lazaridis PI. Estimating the 2D Static Map Based on Moving Stereo Camera. In Ma X, editor, 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8749004 https://doi.org/10.23919/IConAC.2018.8749004