Vision-only autonomous navigation using topometric maps

Dayoub, Feras, Morris, Timothy, Upcroft, Ben, & Corke, Peter (2013) Vision-only autonomous navigation using topometric maps. In International Conference on Intelligent Robots and Systems (2013 IEEE/RSJ), 3-7 November 2013, Tokyo Big Sight, Tokyo.

View at publisher


This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph --- as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

Impact and interest:

6 citations in Scopus
Search Google Scholar™
4 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

295 since deposited on 12 Aug 2013
26 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 61572
Item Type: Conference Paper
Refereed: Yes
Keywords: topometric map, robotics
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 [please consult the author]
Deposited On: 12 Aug 2013 00:21
Last Modified: 11 Apr 2014 07:27

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page