Path Planning of Sidewalks & Street Crossings in Pedestrian Environments Using 2D Map Visual Inference

Ahmed Farid, Takafumi Matsumaru

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This paper describes a path planning framework for processing 2D maps of given pedestrian locations to provide sidewalk paths and street crossing information. The intention is to allow mobile robot platforms to navigate in pedestrian environments without previous knowledge and using only the current location and destination as inputs. Depending on location, current path planning solutions on known 2D maps (e.g. Google Maps and OpenStreetMaps) from both research and industry do not always provide explicit information on sidewalk paths and street crossings, which is a common problem in suburban/rural areas. The framework’s goal is to provide path planning by means of visual inference on 2D map images and search queries through downloadable map data. The results have shown both success and challenges in estimating viable city block paths and street crossings.

Original languageEnglish
Title of host publicationCISM International Centre for Mechanical Sciences, Courses and Lectures
PublisherSpringer International Publishing
Pages247-255
Number of pages9
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameCISM International Centre for Mechanical Sciences, Courses and Lectures
Volume584
ISSN (Print)0254-1971
ISSN (Electronic)2309-3706

ASJC Scopus subject areas

  • Mechanical Engineering
  • Mechanics of Materials
  • Computer Science Applications
  • Modelling and Simulation

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  • Cite this

    Farid, A., & Matsumaru, T. (2019). Path Planning of Sidewalks & Street Crossings in Pedestrian Environments Using 2D Map Visual Inference. In CISM International Centre for Mechanical Sciences, Courses and Lectures (pp. 247-255). (CISM International Centre for Mechanical Sciences, Courses and Lectures; Vol. 584). Springer International Publishing. https://doi.org/10.1007/978-3-319-78963-7_32