Astro Drone: Using crowdsourcing to collect visual data for distance estimation
Keywords
Loading...
Authors
Issue Date
2013-08-23
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Autonomous navigation of robots requires obstacle localization techniques. Obstacles can be localized
based on visual data. Large datasets are needed to test methods for visual obstacle localization
quantitatively. In this thesis, I describe how such a large dataset can be obtained using crowdsourcing.
Contributors collected two types of data while playing a game with a camera-mounted model
helicopter. First, Speed-Up Robust Features (SURFs) [1] were extracted from images taken while the
helicopter approached an object. Second, the distance to the object (ground truth) was calculated
on the basis of helicopter sensor data that is processed by a Kalman filter. I tested if the ground
truth distance measurements to objects are valid and if the proposed method of collecting data using
crowdsourcing is efficient.
To test the validity of distance measurements, I showed that a custom measure based on SURFs
correlated with the distance to an object on manually recorded data. When the measure was calculated
on Astro Drone data, the same measure correlated with the ground truth distances. This indicates that
the ground truth contains valid information about object distances.
To test if crowdsourcing is an efficient way for collecting data, I compare the number of data samples
that were collected by the game (718 in three months) to its development time (1 year). I conclude
that crowdsourcing is not an efficient method for data collection in short-term research projects, but
that long running projects can benefit from crowdsourcing because continued data collection eventually
leads to large datasets.
Description
Citation
Faculty
Faculteit der Sociale Wetenschappen