ICRA 2011 Paper Abstract

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Paper ThP212.1

Guizilini, Vitor (University of Sydney), Ramos, Fabio (University of Sydney)

Visual Odometry Learning for Unmanned Aerial Vehicles

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems IV" (ThP212), Thursday, May 12, 2011, 15:25−15:40, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 18, 2019

Keywords Visual Learning, Field Robots, Aerial Robotics

Abstract

This paper addresses the problem of using visual information to estimate vehicle motion (a.k.a. visual odometry) from a machine learning perspective. The vast majority of current visual odometry algorithms are heavily based on geometry, using a calibrated camera model to recover relative translation (up to scale) and rotation by tracking image features over time. Our method eliminates the need for a parametric model by jointly learning how image structure and vehicle dynamics affect camera motion. This is achieved with a Gaussian Process extension, called Coupled GP, which is trained in a supervised manner to infer the underlying function mapping optical flow to relative translation and rotation. Matched image features parameters are used as inputs and linear and angular velocities are the outputs in our non-linear multi-task regression problem. We show here that it is possible, using a single uncalibrated camera and establishing a first-order temporal dependency between frames, to jointly estimate not only a full 6 DoF motion (along with a full covariance matrix) but also relative scale, a non-trivial problem in monocular configurations. Experiments were performed with imagery collected with an unmanned aerial vehicle (UAV) flying over a deserted area at speeds of 100-120 km/h and altitudes of 80-100 m, a scenario that constitutes a challenge for traditional visual odometry estimators.

 

 

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