Environment Classification and Semantic Grid Map Building Based on Laser Range Finder Data

Shi, L; Kodagoda, S; Dissanayake, G

Environment Classification and Semantic Grid Map Building Based on Laser Range Finder Data

Shi, L

Kodagoda, S

Dissanayake, G

Permalink

Publisher:: online proceeding of IROS 2010 workshop
Publication Type:: Conference Proceeding
Citation:: IROS 2010 Workshop on Semantic Mapping and Autonomous Knowledge Acquisition, 2010, pp. 1 - 6
Issue Date:: 2010-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (507.36 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.editor	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.contributor.editor	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.editor	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date	2010-10-18	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	IROS 2010 Workshop on Semantic Mapping and Autonomous Knowledge Acquisition, 2010, pp. 1 - 6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16322
dc.description.abstract	Human robot interaction has become an important area of research in the robotics community. High level abstractions, which are commonly used by humans, can be learnt by robots to effectively communicate with humans. In this paper, we propose a Semantic Grid Map (SGM) to represent an environment. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. We use a robot-mounted laser range finder (LRF) data to learn and classify an environment into various area types. Then the classification results are combined probabilistically to update the semantic grid map. The classification accuracy is further improved by outlier rejection and topological correction. Finally we present a labeling strategy while a robot is exploring an unknown environment. Experimental results of a robot exploring in a university environment are presented to assess the performance of the algorithm.	en_US
dc.publisher	online proceeding of IROS 2010 workshop	en_US
dc.relation.ispartof	IROS 2010 Workshop on Semantic Mapping and Autonomous Knowledge Acquisition	en_US
dc.relation.ispartof	Workshop on Semantic Mapping and Autonomous Knowledge Acquisition	en_US
dc.title	Environment Classification and Semantic Grid Map Building Based on Laser Range Finder Data	en_US
dc.type	Conference Proceeding
utslib.location	Taipei	en_US
utslib.location.activity	Taipei	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Taipei	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Taipei	en_US
pubs.start-date	2010-10-18	en_US

Abstract:

Human robot interaction has become an important area of research in the robotics community. High level abstractions, which are commonly used by humans, can be learnt by robots to effectively communicate with humans. In this paper, we propose a Semantic Grid Map (SGM) to represent an environment. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. We use a robot-mounted laser range finder (LRF) data to learn and classify an environment into various area types. Then the classification results are combined probabilistically to update the semantic grid map. The classification accuracy is further improved by outlier rejection and topological correction. Finally we present a labeling strategy while a robot is exploring an unknown environment. Experimental results of a robot exploring in a university environment are presented to assess the performance of the algorithm.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16322