Paper Info
Title
A probabilistic framework for stereo-vision based 3D object search with 6D pose estimation
Abstract
This paper presents a method whereby an autonomous mobile robot can search for a 3-dimensional (3D) object using an on-board stereo camera sensor mounted on a pan-tilt head. Search efficiency is realized by the combination of a coarse-scale global search coupled with a fine-scale local search. A grid-based probability map is initially generated using the coarse search, which is based on the color histogram of the desired object. Peaks in the probability map are visited in sequence, where a local (refined) search method based on 3D SIFT features is applied to establish or reject the existence of the desired object, and to update the probability map using Bayesian recursion methods. Once found, the 6D object pose is also estimated. Obstacle avoidance during search can be naturally integrated into the method. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation.
Year
DOI
Venue
2010
10.1109/ROBOT.2010.5509650
Robotics and Automation
Keywords
Field
DocType
Bayes methods,collision avoidance,mobile robots,pose estimation,probability,robot vision,search problems,stereo image processing,3D SIFT feature,3D object search,6D pose estimation,Bayesian recursion method,autonomous mobile robot,coarse-scale global search,fine-scale local search,grid-based probability map,obstacle avoidance,onboard stereo camera sensor,probabilistic framework,stereo-vision
Obstacle avoidance,Computer vision,Stereo camera,Object detection,Color histogram,Beam search,Pose,Artificial intelligence,Local search (optimization),Engineering,Mobile robot
Conference
Volume
Issue
ISSN
2010
1
1050-4729 E-ISBN : 978-1-4244-5040-4
ISBN
Citations 
PageRank 
978-1-4244-5040-4
6
0.50
References 
Authors
8
2
Name
Order
Citations
PageRank
Jeremy Ma11819.93
Burdick, J.W.22988516.87