Keyframe-based visual–inertial odometry using nonlinear optimization
File(s)
Accepted version
Author(s)
Leutenegger, S
Lynen, S
Bosse, M
Siegwart, R
Furgale, P
Type
Journal Article
Abstract
Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy.
Date Issued
2015-03-01
Date Acceptance
2014-12-15
Citation
The International Journal of Robotics Research, 2015, 34 (3), pp.314-334
ISSN
0278-3649
Publisher
SAGE Publications
Start Page
314
End Page
334
Journal / Book Title
The International Journal of Robotics Research
Volume
34
Issue
3
Copyright Statement
© Sage 2014. The final publication is available via Sage at http://dx.doi.org/10.1177/0278364914554813
Subjects
Science & Technology
Technology
Robotics
Visual-inertial odometry
simultaneous localization and mapping (SLAM)
robotics
sensor fusion
stereo camera
inertial measurement unit (IMU)
keyframes
bundle adjustment
OBSERVABILITY ANALYSIS
KALMAN FILTER
VISION
NAVIGATION
ALGORITHM
MOTION
CALIBRATION
EKF
Industrial Engineering & Automation
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Publication Status
Published
Date Publish Online
2014-12-15