Nouranics

Nouranics

About this blog

Here you can find information about the things I work with, details of publications, projects, my resume, and software I have written as well slides from the subjects I used to teach.

Roboethics

NewsPosted by navid nourani Thu, September 27, 2012 14:19:10
An interesting video on Robot Ethics from the Economist.

Roboethics is something I believe we should start considering much earlier in our work habit.

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RSS 2012

NewsPosted by navid nourani Fri, July 13, 2012 13:50:23
A photo of me during my presentation at the Robotics: Science and Systems conference at the University of Sydney on 12 July 2012.

thanks to Michael Milford for the shot :)

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FFT-based Terrain Segmentation for Underwater Mapping

PublicationsPosted by navid nourani Fri, July 13, 2012 05:36:21

Authors

B. Douillard, N. Nourani-Vatani, M. Johnson-Roberson, S. Williams, C. Roman, O. Pizarro, I. Vaughn, G. Inglis

Publication Date

July 2012

Conference

Robotics: Science and Systems, Sydney, Australia

Abstract

A method for segmenting three-dimensional scans of underwater unstructured terrains is presented. Individual terrain scans are represented as an elevation map and analysed using fast Fourier transform (FFT). The segmentation of the ground surface is performed in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground whose segmentation is similar to de-noising / low pass filtering. The cut-off frequency, below which ground frequency components are selected, is automatically determined using peak detection. The user can specify a maximum admissible size of objects (relative to the extent of the scan) to drive the automatic detection of the cut-off frequency. The points above the estimated ground surface are clustered via standard proximity clustering to form object segments. The approach is evaluated using ground truth hand labelled data. It is also evaluated for registration error when the segments are fed as features to an alignment algorithm. In both sets of experiments, the approach is compared to three other segmentation techniques. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.

RSS ePoster Presentation

Paper

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Correlation-based Visual Odometry for Ground Vehicles

ProjectsPosted by navid nourani Fri, March 23, 2012 01:54:12

Introduction

Reliable motion estimation is a key component for autonomous vehicles. Together with my colleague, Dr Paulo Borges, I have presented a visual odometry method for ground vehicles using template matching [1-3]. The method uses a downward facing camera perpendicular to the ground and estimates the motion of the vehicle by analyzing the image shift from frame to frame. Specifically, an image region (template) is selected and using correlation we find the corresponding image region in the next frame.

We have introduced the use of multi-template correlation matching and suggest template quality measures for estimating the suitability of a template for the purpose of correlation. Through an extensive analysis we have determined the expected theoretical error rate of the system and shown its dependence on the template window size and image noise. We have also shown how a linear forward prediction filter can be used to limit the search area to significantly increase the computation performance.

Using a single camera and assuming Ackerman-steering model, the method has been implemented successfully on a large industrial forklift and a 4x4 vehicle. Over 6 km of field trials from our industrial test site, an off-road area and an urban environment were presented illustrating the applicability of the method as an independent sensor for large vehicle motion estimation at practical velocities.

I would love to hear from you if you are using this software and finding it useful or finding bugs (They say ~10 for every 1000 lines of code, so there are probably about 15-17 of these in there!)

You can contact me at n.nourani-vatani@acfr.usyd.edu.au

Enjoy

Navid

[1] Navid Nourani-Vatani and Paulo VK Borges, Correlation-based Visual Odometry for Car-like Vehicles, Journal of Field Robotics, September 2011

[2] Navid Nourani-Vatani, Jonathan Roberts and Mandyam V Srinivasan, Practical Visual Odometry for Car-like Vehicles, IEEE International conference on Robotics and Automation, May 2009

[3] Navid Nourani-Vatani, Jonathan Roberts and Mandyam V Srinivasan, IMU-aided Visual Odometry for Car-like Vehicles, Australasian conference on Robotics and Automation, Dec 2008


The code

Start with the README.txt file.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

There is a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses.

Download the code from GITHUB: https://github.com/nourani/VisualOdo

Download the test data from here [132 MB].

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Ningaloo 2012

NewsPosted by navid nourani Sun, March 04, 2012 11:14:44
I went to Ningaloo reef in Western Australia. We went there to deploy our AUV to survey the status of the reef. One these missions we are working closely with marine scientists from AIMS. Some of the locations our sub is gathering photos from have never ever been captured before... a historic moment!!

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RSS 2012

NewsPosted by navid nourani Tue, January 31, 2012 08:02:07
It became last minute, however, we managed to get the paper in. I'm satisfied. Let's see what the reviewers say...

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Graduated

NewsPosted by navid nourani Fri, January 27, 2012 10:41:42
I have just been notified that my thesis has been accepted!!

I can finally call myself Dr Navid Nourani-Vatani smiley


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Correlation-based Visual Odometry for Ground Vehicles

PublicationsPosted by navid nourani Fri, January 27, 2012 09:56:53

DOI: 10.1002/rob.20407

Authors

Navid Nourani-Vatani and Paulo VK Borges

Publication Date

September 2011

Journal

Journal of Field Robotics

Abstract

Reliable motion estimation is a key component for autonomous vehicles. We present a visual odometry method for ground vehicles using template matching. The method uses a downward facing camera perpendicular to the ground and estimates the motion of the vehicle by analyzing the image shift from frame to frame. Specifically, an image region (template) is selected and using correlation we find the corresponding image region in the next frame. We introduce the use of multi-template correlation matching and suggest template quality measures for estimating the suitability of a template for the purpose of correlation. Several aspects of the template choice are also presented. Through an extensive analysis we derive the expected theoretical error rate of our system and show its dependence on the template window size and image noise. We also show how a linear forward prediction filter can be used to limit the search area to significantly increase the computation performance. Using a single camera and assuming Ackerman-steering model, the method has been implemented successfully on a large industrial forklift and a 4x4 vehicle. Over 6km of field trials from our industrial test site, an off-road area and an urban environment are presented illustrating the applicability of the method as an independent sensor for large vehicle motion estimation at practical velocities.

Video

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