The University of Southampton
University of Southampton Institutional Repository

Depth Estimation for Autonomous Robot Navigation: A Comparative Approach

Depth Estimation for Autonomous Robot Navigation: A Comparative Approach
Depth Estimation for Autonomous Robot Navigation: A Comparative Approach
Depth estimation has long been a fundamental problem both in robotics science and in computer vision. Various methods have been developed and implemented in a large number of applications. Despite the rapid progress in the field the last few years, computation remains a significant issue of the methods employed. In this work, we have implemented two different strategies for inferring depth, both of which are computationally efficient. The first one is inspired by biology, that is optical flow, while the second one is based on a least squares method. In the first strategy, we observe the length variation of the optic flow vectors of a landmark at varying distances and velocities. In the second strategy, we take snapshots of a landmark from different positions and use a least squares approach to estimate the distance between the robot and a landmark. An evaluation of the two different strategies for various depth estimations has been deployed and the results are presented in this paper.
Diamantas, Sotirios
569b0bb8-9d90-447c-8a23-16d38f29444f
Oikonomidis, Anastasios
876dbe2e-cc0e-4620-a72b-2b44ccf32a07
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Diamantas, Sotirios
569b0bb8-9d90-447c-8a23-16d38f29444f
Oikonomidis, Anastasios
876dbe2e-cc0e-4620-a72b-2b44ccf32a07
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023

Diamantas, Sotirios, Oikonomidis, Anastasios and Crowder, Richard (2010) Depth Estimation for Autonomous Robot Navigation: A Comparative Approach. IEEE International Conference on Imaging Systems and Techniques, Thessaloniki, Greece. (Submitted)

Record type: Conference or Workshop Item (Other)

Abstract

Depth estimation has long been a fundamental problem both in robotics science and in computer vision. Various methods have been developed and implemented in a large number of applications. Despite the rapid progress in the field the last few years, computation remains a significant issue of the methods employed. In this work, we have implemented two different strategies for inferring depth, both of which are computationally efficient. The first one is inspired by biology, that is optical flow, while the second one is based on a least squares method. In the first strategy, we observe the length variation of the optic flow vectors of a landmark at varying distances and velocities. In the second strategy, we take snapshots of a landmark from different positions and use a least squares approach to estimate the distance between the robot and a landmark. An evaluation of the two different strategies for various depth estimations has been deployed and the results are presented in this paper.

Text
IEEE_IST2010_finalPaper.pdf - Other
Download (144kB)

More information

Submitted date: 1 July 2010
Additional Information: Event Dates: July, 2010
Venue - Dates: IEEE International Conference on Imaging Systems and Techniques, Thessaloniki, Greece, 2010-07-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 268767
URI: http://eprints.soton.ac.uk/id/eprint/268767
PURE UUID: 62547fec-bc69-407c-b518-e6e893e59014

Catalogue record

Date deposited: 19 Mar 2010 22:14
Last modified: 14 Mar 2024 09:15

Export record

Contributors

Author: Sotirios Diamantas
Author: Anastasios Oikonomidis
Author: Richard Crowder

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×