| Volume 26 Issue 6 - Publication Date: 1 June 2007 |
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| Simultaneous Motion
and Structure
Estimation by Fusion of
Inertial and Vision Data |
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| P. Gemeiner, P. Einramhof, and M. Vincze Automation and Control Institute
Vienna University of Technology
Gusshausstrasse 27-29/376, 1040 Vienna, Austria |
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| For mobile robotics, head gear in augmented reality (AR) applications
or computer vision, it is essential to continuously estimate the
egomotion and the structure of the environment. This paper presents
the system developed in the SmartTracking project, which simultaneously
integrates visual and inertial sensors in a combined estimation
scheme. The sparse structure estimation is based on the detection of
corner features in the environment. From a single known starting position,
the system can move into an unknown environment. The vision
and inertial data are fused, and the performance of both Unscented
Kalman filter and Extended Kalman filter are compared for this task.
The filters are designed to handle asynchronous input from visual
and inertial sensors, which typically operate at different and possibly
varying rates. Additionally, a bank of Extended Kalman filters,
one per corner feature, is used to estimate the position and the quality
of structure points and to include them into the structure estimation
process. The system is demonstrated on a mobile robot executing
known motions, such that the estimation of the egomotion in an unknown
environment can be compared to ground truth. |
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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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1 |
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Example
1: Video of pre-defined motion (1st row
in Tab. 1). |
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2 |
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Example
2: Video of pre-defined motion (3rd row
in Tab. 1). |
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