Difference-in-level Detection from RGB-D Images

Yusuke Nonaka, Hideaki Uchiyama, Hideo Saito, Shoji Yachida, Kota Iwamoto

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


Most robots implicitly assume that the road surface on which they move is flat, without differences in level. Detecting differences in level on roads contributes to robots moving safely without stacking and falling. Although there are some studies on detecting differences in level in RGB or RGB-D images, directly finding only differences in level on roads is difficult due to the abundance and complexity of the types of differences in level on roads. This paper presents a new method for detecting differences in level from RGB-D images obtained by a modern smartphone equipped with a high-performance depth camera. First, we extract a part of differences in level on roads by finding the change of the normal vector in the contour of the detected plane. Then, a deep learning model trained on the dataset created by using the extracted image patches is used for detecting all the differences in level in outdoor images. To evaluate the effectiveness of the proposed method, quantitative and qualitative comparisons with existing methods were conducted. Further, the results from various inputs were qualitatively and quantitatively evaluated. As a result, we verified that the proposed method was able to detect all differences in level in an image, even in complex scenes where existing methods cannot detect.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 17th International Symposium, ISVC 2022, Proceedings
EditorsGeorge Bebis, Bo Li, Angela Yao, Yang Liu, Ye Duan, Manfred Lau, Rajiv Khadka, Ana Crisan, Remco Chang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783031207150
Publication statusPublished - 2022
Event17th International Symposium on Visual Computing, ISVC 2022 - San Diego, United States
Duration: 2022 Oct 32022 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13599 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Symposium on Visual Computing, ISVC 2022
Country/TerritoryUnited States
CitySan Diego


  • Classification
  • Edge detection
  • Segmentation
  • Self-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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