Benchmarking real-time RGBD odometry for light-duty UAVs

Andrew R. Willis; Laith R. Sahawneh; Kevin M. Brink

doi:10.1117/12.2225534

1 June 2016 Benchmarking real-time RGBD odometry for light-duty UAVs

Andrew R. Willis, Laith R. Sahawneh, Kevin M. Brink

Proceedings Volume 9867, Three-Dimensional Imaging, Visualization, and Display 2016; 98670O (2016) https://doi.org/10.1117/12.2225534
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Andrew R. Willis, Laith R. Sahawneh, and Kevin M. Brink "Benchmarking real-time RGBD odometry for light-duty UAVs", Proc. SPIE 9867, Three-Dimensional Imaging, Visualization, and Display 2016, 98670O (1 June 2016); https://doi.org/10.1117/12.2225534

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