Visual inertial odometry for quadrotors on SE(3)

Giuseppe Loianno, Michael Watterson, Vijay Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1544-1551
Number of pages8
Volume2016-June
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period5/16/165/21/16

Fingerprint

Lie groups
Kalman filters
Robotics
Antennas
Gyroscopes
Sensors
State estimation
Accelerometers
Algebra
Global positioning system
Cameras
Costs

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Loianno, G., Watterson, M., & Kumar, V. (2016). Visual inertial odometry for quadrotors on SE(3). In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (Vol. 2016-June, pp. 1544-1551). [7487292] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487292

Visual inertial odometry for quadrotors on SE(3). / Loianno, Giuseppe; Watterson, Michael; Kumar, Vijay.

2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. p. 1544-1551 7487292.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Loianno, G, Watterson, M & Kumar, V 2016, Visual inertial odometry for quadrotors on SE(3). in 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. vol. 2016-June, 7487292, Institute of Electrical and Electronics Engineers Inc., pp. 1544-1551, 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, Sweden, 5/16/16. https://doi.org/10.1109/ICRA.2016.7487292
Loianno G, Watterson M, Kumar V. Visual inertial odometry for quadrotors on SE(3). In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1544-1551. 7487292 https://doi.org/10.1109/ICRA.2016.7487292
Loianno, Giuseppe ; Watterson, Michael ; Kumar, Vijay. / Visual inertial odometry for quadrotors on SE(3). 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1544-1551
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