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3D SLAM in texture-less environments using rank order statistics

Published online by Cambridge University Press:  21 October 2015

Khalid Yousif,
Alireza Bab-HadiasharOpen the ORCID record for Alireza Bab-Hadiashar[Opens in a new window] and
Reza Hoseinnezhad
Khalid Yousif*
Affiliation:
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC 3001, Australia
Alireza Bab-Hadiashar
Affiliation:
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC 3001, Australia
Reza Hoseinnezhad
Affiliation:
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC 3001, Australia
*
*Corresponding author. E-mail: s3362555@student.rmit.edu.au; abh@rmit.edu.au; rezah@rmit.edu.au
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Summary

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We present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from sequential frames are then matched and a rank order statistics based robust estimator is employed to refine the matches and estimate a rigid-body transformation between the frames. Experimental evaluations show that the proposed keypoint extraction method is highly repeatable and outperforms the state of the art methods in terms of accuracy and repeatability. We show that the performance of the registration algorithm is also comparable to other well-known methods in texture-less environments.

Keywords

SLAMLocalizationMappingRGB-DTexture-less

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 4 , April 2017, pp. 809 - 831
Copyright
Copyright © Cambridge University Press 2015 

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