Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model
Road detection is an important task in autonomous navigation systems. In this paper, the authors propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, the authors transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, the authors apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, the authors transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Gu, Shuo
- Zhang, Yigong
- Tang, Jinhui
- Yang, Jian
- Alvarez, Jose M
- Kong, Hui
- Publication Date: 2019-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 11635-11645
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 68
- Issue Number: 12
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Cameras; Detection and identification; Estimating; Laser radar; Roads; Task analysis; Three dimensional displays
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01726720
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 26 2019 4:05PM