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Simultaneous state initialization and gyroscope bias calibration in visual inertial aided navigation


Kaiser, Jacques; Martinelli, Agostino; Fontana, Flavio; Scaramuzza, Davide (2017). Simultaneous state initialization and gyroscope bias calibration in visual inertial aided navigation. IEEE Robotics and Automation Letters, 2(1):18-25.

Abstract

State of the art approaches for visual-inertial sensor fusion use filter-based or optimization-based algorithms. Due to the nonlinearity of the system, a poor initialization can have a dramatic impact on the performance of these estimation methods. Recently, a closed-form solution providing such an initialization was derived in [1]. That solution determines the velocity (angular and linear) of a monocular camera in metric units by only using inertial measurements and image features acquired in a short time interval. In this letter, we study the impact of noisy sensors on the performance of this closed-form solution. We show that the gyroscope bias, not accounted for in [1], significantly affects the performance of the method. Therefore, we introduce a new method to automatically estimate this bias. Compared to the original method, the new approach now models the gyroscope bias and is robust to it. The performance of the proposed approach is successfully demonstrated on real data from a quadrotor MAV.

Abstract

State of the art approaches for visual-inertial sensor fusion use filter-based or optimization-based algorithms. Due to the nonlinearity of the system, a poor initialization can have a dramatic impact on the performance of these estimation methods. Recently, a closed-form solution providing such an initialization was derived in [1]. That solution determines the velocity (angular and linear) of a monocular camera in metric units by only using inertial measurements and image features acquired in a short time interval. In this letter, we study the impact of noisy sensors on the performance of this closed-form solution. We show that the gyroscope bias, not accounted for in [1], significantly affects the performance of the method. Therefore, we introduce a new method to automatically estimate this bias. Compared to the original method, the new approach now models the gyroscope bias and is robust to it. The performance of the proposed approach is successfully demonstrated on real data from a quadrotor MAV.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:calibration;filtering theory;gyroscopes;inertial navigation;mobile robots;optimisation;sensor fusion;space vehicles;autonomous mobile robots;closed-form solution;filter-based algorithms;gyroscope bias calibration;image features;inertial measurements;metric units;micro aerial vehicles;monocular camera;noisy sensors;optimization-based algorithms;quadrotor MAV;sensor fusion;simultaneous state initialization;visual inertial aided navigation;Calibration;Cameras;Closed-form solutions;Gyroscopes;Linear systems;Robot sensing systems;Visualization;Localization;Sensor Fusion;Sensor fusion;Visual-Based Navigation;localization;visual-based navigation
Language:English
Date:25 January 2017
Deposited On:12 Aug 2016 06:07
Last Modified:08 Dec 2017 20:09
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
Additional Information:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/LRA.2016.2521413
Related URLs:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7390213&isnumber=7451292 (Publisher)
Other Identification Number:merlin-id:13323

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