VLASE: Vehicle Localization by Aggregating Semantic Edges

Xin Yu, Sagar Chaturvedi, Chen Feng, Yuichi Taguchi, Teng Yok Lee, Clinton Fernandes, Srikumar Ramalingam

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

Abstract

We propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road-sidewalk, and building-ground. While prior work has shown promising results by utilizing the boundary between prominent classes such as sky and building using skylines, we generalize this to consider 19 semantic classes. We extract semantic edge features using CASENet architecture and utilize VLAD framework to perform image retrieval. We achieve improvement over state-of-the-art localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. Ablation study shows the importance of different semantic classes, and our unified approach achieves better performance compared to individual prominent features such as skylines. We also introduce SLC Marathon dataset, a challenging dataset covering most of Salt Lake City with sufficient lighting variations.

Original languageEnglish (US)
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3196-3203
Number of pages8
ISBN (Electronic)9781538680940
DOIs
StatePublished - Dec 27 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: Oct 1 2018Oct 5 2018

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
CountrySpain
CityMadrid
Period10/1/1810/5/18

    Fingerprint

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T. Y., Fernandes, C., & Ramalingam, S. (2018). VLASE: Vehicle Localization by Aggregating Semantic Edges. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 (pp. 3196-3203). [8594358] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2018.8594358