Reliable Monocular Ego-Motion Estimation System in Rainy Urban Environments

Huaiyang Huang, Yuxiang Sun, Ming Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Visual Simultaneous Localization and Mapping (SLAM) systems assume a static world. They usually fail under adverse weather conditions. In this paper, we propose a robust monocular SLAM system that is able to work under rainy conditions in urban environments reliably. To recover camera ego-motion from images with rain streaks, we apply a superpixel-based image content alignment method for the static background modelling. With coarse outputs estimated through averaging temporal matches, image details are recovered by a Convolutional Neural Network (CNN). Based on the statistic distribution of intensity variance between original and reconstructed image pairs, a robust and noise-sensitive weight function is explored for rejecting outliers when estimating camera poses. Quantitative evaluation results on the CARLA and synthetic KITTI datasets demonstrate the reliability of the proposed system and its superiority over the state-of-the-art approaches.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherIEEE
Pages1290-1297
Number of pages8
ISBN (Electronic)9781538670248
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Country/TerritoryNew Zealand
CityAuckland
Period27/10/1930/10/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management Science and Operations Research
  • Instrumentation
  • Transportation

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