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Feature based stereo correspondence using moment invariant

Conference Paper


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Abstract


  • Autonomous navigation is seen as a vital tool in harnessing the enormous potential of Unmanned Aerial Vehicles (UAV) and small robotic vehicles for both military and civilian use. Even though, laser based scanning solutions for Simultaneous Location And Mapping (SLAM) is considered as the most reliable for depth estimation, they are not feasible for use in UAV and land-based small vehicles due to their physical

    size and weight. Stereovision is considered as the best approach for any autonomous navigation solution as stereo rigs are considered to be lightweight and inexpensive. However, stereoscopy which estimates the depth information through pairs of stereo images can still be computationally expensive and unreliable. This is mainly due to some of the algorithms used in successful stereovision solutions require high computational requirements that cannot be met by small robotic vehicles. In our research, we implement a feature-based stereovision solution using moment invariants as a metric to find corresponding regions in image pairs that will reduce the computational complexity and improve the accuracy of the disparity measures that will be significant for the use in UAVs and in small robotic

    vehicles.

Publication Date


  • 2008

Citation


  • Premaratne, P. & Safaei, F. 2008, ''Feature based stereo correspondence using moment invariant'', International Conference on Information and Automation for Sustainability, IEEE Region 10 and ICIAFS, Colombo, Sri Lanka, pp. 104-108.

Scopus Eid


  • 2-s2.0-64349086388

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2687&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1667

Has Global Citation Frequency


Start Page


  • 104

End Page


  • 108

Place Of Publication


  • Colombo, Sri Lanka

Abstract


  • Autonomous navigation is seen as a vital tool in harnessing the enormous potential of Unmanned Aerial Vehicles (UAV) and small robotic vehicles for both military and civilian use. Even though, laser based scanning solutions for Simultaneous Location And Mapping (SLAM) is considered as the most reliable for depth estimation, they are not feasible for use in UAV and land-based small vehicles due to their physical

    size and weight. Stereovision is considered as the best approach for any autonomous navigation solution as stereo rigs are considered to be lightweight and inexpensive. However, stereoscopy which estimates the depth information through pairs of stereo images can still be computationally expensive and unreliable. This is mainly due to some of the algorithms used in successful stereovision solutions require high computational requirements that cannot be met by small robotic vehicles. In our research, we implement a feature-based stereovision solution using moment invariants as a metric to find corresponding regions in image pairs that will reduce the computational complexity and improve the accuracy of the disparity measures that will be significant for the use in UAVs and in small robotic

    vehicles.

Publication Date


  • 2008

Citation


  • Premaratne, P. & Safaei, F. 2008, ''Feature based stereo correspondence using moment invariant'', International Conference on Information and Automation for Sustainability, IEEE Region 10 and ICIAFS, Colombo, Sri Lanka, pp. 104-108.

Scopus Eid


  • 2-s2.0-64349086388

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2687&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1667

Has Global Citation Frequency


Start Page


  • 104

End Page


  • 108

Place Of Publication


  • Colombo, Sri Lanka