A benchmarking tool for MAV visual pose estimation

The large collections of datasets for researchers working on the Simultaneous Localization and Mapping problem are mostly collected from sensors such as wheel encoders and laser range finders mounted on ground robots. The recent growing interest in doing visual pose estimation with cameras mounted o...

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Bibliographic Details
Published in2010 11th International Conference on Control Automation Robotics and Vision pp. 1541 - 1546
Main Authors Gim Hee Lee, Achtelik, M, Fraundorfer, F, Pollefeys, M, Siegwart, R
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Benchmarking Tool
Calibration
Cameras
Estimation
Micro-Aerial Vehicles
Quadrotor
Sensors
Synchronization
Trajectory
Visual SLAM
Visualization
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ISBN1424478146
9781424478149
DOI10.1109/ICARCV.2010.5707339

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Summary:The large collections of datasets for researchers working on the Simultaneous Localization and Mapping problem are mostly collected from sensors such as wheel encoders and laser range finders mounted on ground robots. The recent growing interest in doing visual pose estimation with cameras mounted on micro-aerial vehicles however has made these datasets less useful. In this paper, we describe our work in creating new datasets collected from a sensor suite mounted on a quadrotor platform. Our sensor suite includes a forward looking camera, a downward looking camera, an inertial measurement unit and a Vicon system for groundtruth. We propose the use our datasets as benchmarking tools for future works on visual pose estimation for micro-aerial vehicles. We also show examples of how the datasets could be used for benchmarking visual pose estimation algorithms.
ISBN:1424478146
9781424478149
DOI:10.1109/ICARCV.2010.5707339