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Paper TuB01.5

Khan, Yasir Niaz (University of Tübingen), Masselli, Andreas (University of Tübingen), Zell, Andreas (University of Tübingen)

Visual Terrain Classification by Flying Robots

Scheduled for presentation during the Regular Session "Control and Planning for UAVs" (TuB01), Tuesday, May 15, 2012, 11:30−11:45, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Aerial Robotics, Computer Vision for Robotics and Automation, Omnidirectional Vision

Abstract

In this paper we investigate the effectiveness of SURF features for visual terrain classification for outdoor flying robots. A quadrocopter fitted with a single camera is flown over different terrains to take images of the ground below. Each image is divided into a grid and SURF features are calculated at grid intersections. A classifier is then used to learn to differentiate between different terrain types. Classification results of the SURF descriptor are compared with results from other texture descriptors like Local Binary Patterns and Local Ternary Patterns. Six different terrain types are considered in this approcah. Random forests are used for classification on each descriptor. It is shown that SURF features perform better than other descriptors at higher resolutions.

 

 

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