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Proceedings Paper

Benchmarking real-time RGBD odometry for light-duty UAVs
Author(s): Andrew R. Willis; Laith R. Sahawneh; Kevin M. Brink
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Paper Abstract

This article describes the theoretical and implementation challenges associated with generating 3D odometry estimates (delta-pose) from RGBD sensor data in real-time to facilitate navigation in cluttered indoor environments. The underlying odometry algorithm applies to general 6DoF motion; however, the computational platforms, trajectories, and scene content are motivated by their intended use on indoor, light-duty UAVs. Discussion outlines the overall software pipeline for sensor processing and details how algorithm choices for the underlying feature detection and correspondence computation impact the real-time performance and accuracy of the estimated odometry and associated covariance. This article also explores the consistency of odometry covariance estimates and the correlation between successive odometry estimates. The analysis is intended to provide users information needed to better leverage RGBD odometry within the constraints of their systems.

Paper Details

Date Published: 1 June 2016
PDF: 17 pages
Proc. SPIE 9867, Three-Dimensional Imaging, Visualization, and Display 2016, 98670O (1 June 2016); doi: 10.1117/12.2225534
Show Author Affiliations
Andrew R. Willis, Univ. of North Carolina at Charlotte (United States)
Laith R. Sahawneh, Univ. of Florida (United States)
Kevin M. Brink, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9867:
Three-Dimensional Imaging, Visualization, and Display 2016
Bahram Javidi; Jung-Young Son, Editor(s)

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