Pre-Robotic Navigation Identification of Pedestrian Crossings & Their Orientations

EasyChair Preprint 1448

Abstract

This paper describes an off-line (i.e. pre-navigation) methodology for machines/robots to identify zebra crossings and their respective orientations within pedestrian environments, for the purpose of identifying street crossing ability. Not knowing crossing ability beforehand can prevent motion trajectories from being accurately planned pre-navigation. As such, we propose a methodology that sources information from internet 2D maps to identify the locations of pedestrian zebra crossings. This information is comprised of road networks and satellite imagery of street intersections, from which the locations and orientations of zebra crossings can be identified by means of trained neural networks and proposed verification algorithms. The methodology demonstrated good capability in detecting and mapping zebra crossings' locations, while also showing good results in verifying them against falsely detected objects in satellite imagery. Orientation estimation of zebra crossings, using a proposed line-scanning algorithm, was found to be within an error range of 4 degrees on a limited test set.

Keyphrases: 2D internet maps, Pedestrian Environments, Street Crossings, object detection

@booklet{EasyChair:1448,
  author    = {Ahmed Farid and Takafumi Matsumaru},
  title     = {Pre-Robotic Navigation Identification of Pedestrian Crossings & Their Orientations},
  howpublished = {EasyChair Preprint 1448},
  year      = {EasyChair, 2019}}