LiDAR-camera fusion for road detection using a recurrent conditional random field model

Reliable road detection is an essential task in autonomous driving systems. Two categories of sensors are commonly used, cameras and light detection and ranging (LiDAR), each of which can provide corresponding supplements. Nevertheless, existing sensor fusion methods do not fully utilize multimodal data. Most of them are dominated by images and take point clouds as a supplement rather than making the best of them, and the correlation between modalities is ignored. This paper proposes a recurrent conditional random field (R-CRF) model to fuse images and point clouds for road detection. The R-CRF model integrates results (information) from modalities in a probabilistic way. Each modality is independently processed with its semantic segmentation network. The probability scores obtained are considered a unary term for individual pixel nodes in a random field, while RGB images and the densified LiDAR images are used as pairwise terms. The energy function is then iteratively optimized by mean-field variational inference, and the labelling results are refined by exploiting fully connected graphs of the RGB image and LiDAR images. Extensive experiments are conducted on the public KITTI-Road dataset, and the proposed method achieves competitive performance.

• Record URL:
  http://dx.doi.org/10.1038/s41598-022-14438-w
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20452322
• Supplemental Notes:
  • Copyright © 2022. The Author(s).
• Authors:
  • Wang, Lele
  • Huang, Yingping
• Publication Date: 2022

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: 11320
• Serial:
  • Scientific Reports
  • Volume: 12
  • Issue Number: 1
  • Publisher: Nature Publishing Group
  • ISSN: 2045-2322
  • Serial URL: http://www.nature.com/srep/index.html
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Cameras; Detection and identification technologies; Laser radar; Sensors; Traffic lanes
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01861153
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 14 2022 8:53AM