IEICE TRANSACTIONS on Information
Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment
Summary :
We propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.5 pp.1232-1242
- Publication Date
- 2018/05/01
- Publicized
- 2018/02/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017MVP0006
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision and its Applications)
- Category
- Machine Vision and its Applications
Authors
Atsushi KAWASAKI
Keio University
Kosuke HARA
Denso IT Laboratory
Hideo SAITO
Keio University
Keyword
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Atsushi KAWASAKI, Kosuke HARA, Hideo SAITO, "Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 5, pp. 1232-1242, May 2018, doi: 10.1587/transinf.2017MVP0006.
Abstract: We propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017MVP0006/_p
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@ARTICLE{e101-d_5_1232,
author={Atsushi KAWASAKI, Kosuke HARA, Hideo SAITO, },
journal={IEICE TRANSACTIONS on Information},
title={Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment},
year={2018},
volume={E101-D},
number={5},
pages={1232-1242},
abstract={We propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.},
keywords={},
doi={10.1587/transinf.2017MVP0006},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment
T2 - IEICE TRANSACTIONS on Information
SP - 1232
EP - 1242
AU - Atsushi KAWASAKI
AU - Kosuke HARA
AU - Hideo SAITO
PY - 2018
DO - 10.1587/transinf.2017MVP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2018
AB - We propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.
ER -
English
