Title
Visual-inertial self-calibration on informative motion segments.
Abstract
Environmental conditions and external effects, such as shocks, have a significant impact on the calibration parameters of visual-inertial sensor systems. Thus long-term operation of these systems cannot fully rely on factory calibration. Since the observability of certain parameters is highly dependent on the motion of the device, using short data segments at device initialization may yield poor results. When such systems are additionally subject to energy constraints, it is also infeasible to use full-batch approaches on a big dataset and careful selection of the data is of high importance. In this paper, we present a novel approach for resource efficient self-calibration of visual-inertial sensor systems. This is achieved by casting the calibration as a segment-based optimization problem that can be run on a small subset of informative segments. Consequently, the computational burden is limited as only a predefined number of segments is used. We also propose an efficient information-theoretic selection to identify such informative motion segments. In evaluations on a challenging dataset, we show our approach to significantly outperform state-of-the-art in terms of computational burden while maintaining a comparable accuracy.
Year
DOI
Venue
2017
10.1109/icra.2017.7989766
ICRA
DocType
Volume
ISSN
Journal
abs/1708.02382
Robotics and Automation (ICRA), 2017 IEEE International Conference on
Citations 
PageRank 
References 
1
0.35
7
Authors
6
Name
Order
Citations
PageRank
Thomas Schneider118910.37
Mingyang Li227017.60
M. Burri334318.62
Juan I. Nieto493988.52
Roland Siegwart57640551.49
Igor Gilitschenski6314.05