IMU-Based Inertia Estimation for a Quadrotor Using Newton-Euler Dynamics

James Svacha; James Paulos; Giuseppe Loianno; Vijay Kumar

doi:10.1109/LRA.2020.2976308

IMU-Based Inertia Estimation for a Quadrotor Using Newton-Euler Dynamics

James Svacha, James Paulos, Giuseppe Loianno, Vijay Kumar

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In this letter, we demonstrate that a quadrotor's tilt, angular velocity, linear velocity and the parameters shown in Table II may be estimated using only an inertial measurement unit (IMU) and motor speed feedback for sensing. Motor speed commands are used to drive the process model and the motor speed and IMU measurements are used in the measurement model of an unscented Kalman filter (UKF) containing 32 states, 14 of which are constant parameters. We analytically show the observability of this system. Furthermore, we demonstrate through experiments that a blade flapping moment term is not only significant, but necessary to include in the rotation dynamics to get a sensible moment of inertia estimate. We also model the motor torque as a function of the angular acceleration and velocity of the motors in order to obtain a more accurate moment of inertia estimate.

Original language	English (US)
Article number	9013062
Pages (from-to)	3861-3867
Number of pages	7
Journal	IEEE Robotics and Automation Letters
Volume	5
Issue number	3
DOIs	https://doi.org/10.1109/LRA.2020.2976308
State	Published - Jul 2020

Keywords

Aerodynamics
indoor navigation
inertial navigation
state estimation
unmanned aerial vehicles

ASJC Scopus subject areas

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

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title = "IMU-Based Inertia Estimation for a Quadrotor Using Newton-Euler Dynamics",

abstract = "In this letter, we demonstrate that a quadrotor's tilt, angular velocity, linear velocity and the parameters shown in Table II may be estimated using only an inertial measurement unit (IMU) and motor speed feedback for sensing. Motor speed commands are used to drive the process model and the motor speed and IMU measurements are used in the measurement model of an unscented Kalman filter (UKF) containing 32 states, 14 of which are constant parameters. We analytically show the observability of this system. Furthermore, we demonstrate through experiments that a blade flapping moment term is not only significant, but necessary to include in the rotation dynamics to get a sensible moment of inertia estimate. We also model the motor torque as a function of the angular acceleration and velocity of the motors in order to obtain a more accurate moment of inertia estimate.",

keywords = "Aerodynamics, indoor navigation, inertial navigation, state estimation, unmanned aerial vehicles",

author = "James Svacha and James Paulos and Giuseppe Loianno and Vijay Kumar",

note = "Funding Information: Manuscript received September 8, 2019; accepted February 2, 2020. Date of publication February 26, 2020; date of current version April 17, 2020. This letter was recommended for publication by Associate Editor I. Sa and Editor J. Roberts upon evaluation of the reviewers{\textquoteright} comments. This work was supported in part by the ARL under Grant DCIST CRA W911NF-17-2-0181, in part by Qualcomm Research, NSF under Grants CNS-1446592 and CNS-1521617, and in part by ARO under Grant W911NF-13-1-0350. (Corresponding author: James Svacha.) James Svacha, James Paulos, and Vijay Kumar are with the GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: jsvacha@seas.upenn.edu; jpaulos@seas.upenn.edu; kumar@seas.upenn.edu).",

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language = "English (US)",

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AU - Svacha, James

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AU - Loianno, Giuseppe

AU - Kumar, Vijay

N1 - Funding Information: Manuscript received September 8, 2019; accepted February 2, 2020. Date of publication February 26, 2020; date of current version April 17, 2020. This letter was recommended for publication by Associate Editor I. Sa and Editor J. Roberts upon evaluation of the reviewers’ comments. This work was supported in part by the ARL under Grant DCIST CRA W911NF-17-2-0181, in part by Qualcomm Research, NSF under Grants CNS-1446592 and CNS-1521617, and in part by ARO under Grant W911NF-13-1-0350. (Corresponding author: James Svacha.) James Svacha, James Paulos, and Vijay Kumar are with the GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: jsvacha@seas.upenn.edu; jpaulos@seas.upenn.edu; kumar@seas.upenn.edu).

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N2 - In this letter, we demonstrate that a quadrotor's tilt, angular velocity, linear velocity and the parameters shown in Table II may be estimated using only an inertial measurement unit (IMU) and motor speed feedback for sensing. Motor speed commands are used to drive the process model and the motor speed and IMU measurements are used in the measurement model of an unscented Kalman filter (UKF) containing 32 states, 14 of which are constant parameters. We analytically show the observability of this system. Furthermore, we demonstrate through experiments that a blade flapping moment term is not only significant, but necessary to include in the rotation dynamics to get a sensible moment of inertia estimate. We also model the motor torque as a function of the angular acceleration and velocity of the motors in order to obtain a more accurate moment of inertia estimate.

AB - In this letter, we demonstrate that a quadrotor's tilt, angular velocity, linear velocity and the parameters shown in Table II may be estimated using only an inertial measurement unit (IMU) and motor speed feedback for sensing. Motor speed commands are used to drive the process model and the motor speed and IMU measurements are used in the measurement model of an unscented Kalman filter (UKF) containing 32 states, 14 of which are constant parameters. We analytically show the observability of this system. Furthermore, we demonstrate through experiments that a blade flapping moment term is not only significant, but necessary to include in the rotation dynamics to get a sensible moment of inertia estimate. We also model the motor torque as a function of the angular acceleration and velocity of the motors in order to obtain a more accurate moment of inertia estimate.

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KW - indoor navigation

KW - inertial navigation

KW - state estimation

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IMU-Based Inertia Estimation for a Quadrotor Using Newton-Euler Dynamics

Abstract

Keywords

ASJC Scopus subject areas

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