LIDAR and monocular based overhanging obstacle detection

Young, J and Simic, M 2015, 'LIDAR and monocular based overhanging obstacle detection', Procedia Computer Science, vol. 60, pp. 1423-1432.

Document type: Journal Article
Collection: Journal Articles

Attached Files
Name Description MIMEType Size
n2006055620.pdf Published Version application/pdf 1.27MB
Title LIDAR and monocular based overhanging obstacle detection
Author(s) Young, J
Simic, M
Year 2015
Journal name Procedia Computer Science
Volume number 60
Start page 1423
End page 1432
Total pages 10
Publisher Elsevier BV
Abstract This paper presents an improved method for the detection of obstacles in the trajectory of autonomous ground vehicle (AGV). The novel approach requires fewer calculations, i.e. less computational time. Obstacle detection algorithms were investigated, in order to perform safe motion control, in an environment with unknown overhanging obstacles. We describe a two dimensional (2D) laser sensor application, and optimal sensor configurations for mounting a monocular camera to monitor path ahead clearance. Two different sensors are used, a vision sensor and a scanning laser, Light Detection and Ranging (LIDAR). While LIDAR measures the precise distance to the object, it cannot detect low objects and overhanging obstacles due to its predefined, constant, scanning height and angle. In contrast, vision sensor provides 2D scenery information with relatively poor distance information. To compensate for the drawbacks of these two sensors, the sensor fusion method for obstacle detection of AGV is proposed. Size expansion cue algorithm is deployed to achieve that goal. Proposed method is validated experimentally.
Subject Control Systems, Robotics and Automation
Keyword(s) LIDAR
Monocular camera
Overhanging obstacles detection
Scale invariant feature transform (SIFT)
DOI - identifier 10.1016/j.procs.2015.08.218
Copyright notice © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
ISSN 1877-0509
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 54 Abstract Views, 68 File Downloads  -  Detailed Statistics
Created: Wed, 28 Oct 2015, 10:15:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us