Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/7635
Title: A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments
Authors: Yu, Qian 
Araújo, Helder 
Wang, Hong 
Issue Date: 2005
Citation: Autonomous Robots. 19:2 (2005) 141-157
Abstract: Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.
URI: http://hdl.handle.net/10316/7635
DOI: 10.1007/s10514-005-0612-6
Rights: openAccess
Appears in Collections:FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
obra.pdf1.51 MBAdobe PDFView/Open
Show full item record

Page view(s) 10

728
checked on Oct 16, 2019

Download(s) 50

224
checked on Oct 16, 2019

Google ScholarTM

Check

Altmetric

Dimensions


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.