Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter

Houwu Bai, Eric Wan, Xubo Song, Andriy Myronenko, Alexander Bogdanov

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

2 Citations (Scopus)

Abstract

This paper presents the vision-only navigation and control of a small autonomous helicopter given only measurements from a video camera fixed on the ground. The goal is to develop an alternative to traditional INS/GPS and on-board vision aided systems. The autonomous navigation and control of the helicopter is achieved using a nonlinear state estimator and a state-dependent controller. A key difference to INS/GPS navigation is that measurements of the helicopter's accelerations and angular velocities are not directly available. The state estimation combines the vision measurements with a dynamic model of the vehicle in a recursive filtering procedure using a Sigma-Point Kalman Filter (SPKF). The estimation of the helicopter's current state (position, attitude, velocity, and angular velocity) is then fed back in real-time to a state-dependent Riccati equation (SDRE) controller to generate radio control commands to the helicopter. Simulations are provided comparing performance relative to INS/GPS navigation. Experiments also show that an accurate dynamic model of the vehicle is necessary for closed-loop stability. Our results indicate the feasibility of designing a vision-only estimation and control system capable of stabilizing and maneuvering a small unmanned helicopter. Other than simple on-board avionics for low level actuator control, the ground station is responsible for video capture, state-estimation, and state-feedback flight control.

Original languageEnglish (US)
Title of host publicationProceedings of the Institute of Navigation, National Technical Meeting
Pages1264-1275
Number of pages12
Volume2
StatePublished - 2007
EventInstitute of Navigation National Technical Meeting, NTM 2007 - San Diego, CA, United States
Duration: Jan 22 2007Jan 24 2007

Other

OtherInstitute of Navigation National Technical Meeting, NTM 2007
CountryUnited States
CitySan Diego, CA
Period1/22/071/24/07

Fingerprint

Unmanned aerial vehicles (UAV)
Helicopters
Kalman filters
Navigation
Global positioning system
Angular velocity
State estimation
Dynamic models
Controllers
Antenna grounds
Riccati equations
Avionics
Video cameras
State feedback
Actuators
Control systems
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Bai, H., Wan, E., Song, X., Myronenko, A., & Bogdanov, A. (2007). Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter. In Proceedings of the Institute of Navigation, National Technical Meeting (Vol. 2, pp. 1264-1275)

Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter. / Bai, Houwu; Wan, Eric; Song, Xubo; Myronenko, Andriy; Bogdanov, Alexander.

Proceedings of the Institute of Navigation, National Technical Meeting. Vol. 2 2007. p. 1264-1275.

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

Bai, H, Wan, E, Song, X, Myronenko, A & Bogdanov, A 2007, Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter. in Proceedings of the Institute of Navigation, National Technical Meeting. vol. 2, pp. 1264-1275, Institute of Navigation National Technical Meeting, NTM 2007, San Diego, CA, United States, 1/22/07.
Bai H, Wan E, Song X, Myronenko A, Bogdanov A. Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter. In Proceedings of the Institute of Navigation, National Technical Meeting. Vol. 2. 2007. p. 1264-1275
Bai, Houwu ; Wan, Eric ; Song, Xubo ; Myronenko, Andriy ; Bogdanov, Alexander. / Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter. Proceedings of the Institute of Navigation, National Technical Meeting. Vol. 2 2007. pp. 1264-1275
@inproceedings{444a036a2aa14abaa2abdefc0534e5fe,
title = "Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter",
abstract = "This paper presents the vision-only navigation and control of a small autonomous helicopter given only measurements from a video camera fixed on the ground. The goal is to develop an alternative to traditional INS/GPS and on-board vision aided systems. The autonomous navigation and control of the helicopter is achieved using a nonlinear state estimator and a state-dependent controller. A key difference to INS/GPS navigation is that measurements of the helicopter's accelerations and angular velocities are not directly available. The state estimation combines the vision measurements with a dynamic model of the vehicle in a recursive filtering procedure using a Sigma-Point Kalman Filter (SPKF). The estimation of the helicopter's current state (position, attitude, velocity, and angular velocity) is then fed back in real-time to a state-dependent Riccati equation (SDRE) controller to generate radio control commands to the helicopter. Simulations are provided comparing performance relative to INS/GPS navigation. Experiments also show that an accurate dynamic model of the vehicle is necessary for closed-loop stability. Our results indicate the feasibility of designing a vision-only estimation and control system capable of stabilizing and maneuvering a small unmanned helicopter. Other than simple on-board avionics for low level actuator control, the ground station is responsible for video capture, state-estimation, and state-feedback flight control.",
author = "Houwu Bai and Eric Wan and Xubo Song and Andriy Myronenko and Alexander Bogdanov",
year = "2007",
language = "English (US)",
volume = "2",
pages = "1264--1275",
booktitle = "Proceedings of the Institute of Navigation, National Technical Meeting",

}

TY - GEN

T1 - Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter

AU - Bai, Houwu

AU - Wan, Eric

AU - Song, Xubo

AU - Myronenko, Andriy

AU - Bogdanov, Alexander

PY - 2007

Y1 - 2007

N2 - This paper presents the vision-only navigation and control of a small autonomous helicopter given only measurements from a video camera fixed on the ground. The goal is to develop an alternative to traditional INS/GPS and on-board vision aided systems. The autonomous navigation and control of the helicopter is achieved using a nonlinear state estimator and a state-dependent controller. A key difference to INS/GPS navigation is that measurements of the helicopter's accelerations and angular velocities are not directly available. The state estimation combines the vision measurements with a dynamic model of the vehicle in a recursive filtering procedure using a Sigma-Point Kalman Filter (SPKF). The estimation of the helicopter's current state (position, attitude, velocity, and angular velocity) is then fed back in real-time to a state-dependent Riccati equation (SDRE) controller to generate radio control commands to the helicopter. Simulations are provided comparing performance relative to INS/GPS navigation. Experiments also show that an accurate dynamic model of the vehicle is necessary for closed-loop stability. Our results indicate the feasibility of designing a vision-only estimation and control system capable of stabilizing and maneuvering a small unmanned helicopter. Other than simple on-board avionics for low level actuator control, the ground station is responsible for video capture, state-estimation, and state-feedback flight control.

AB - This paper presents the vision-only navigation and control of a small autonomous helicopter given only measurements from a video camera fixed on the ground. The goal is to develop an alternative to traditional INS/GPS and on-board vision aided systems. The autonomous navigation and control of the helicopter is achieved using a nonlinear state estimator and a state-dependent controller. A key difference to INS/GPS navigation is that measurements of the helicopter's accelerations and angular velocities are not directly available. The state estimation combines the vision measurements with a dynamic model of the vehicle in a recursive filtering procedure using a Sigma-Point Kalman Filter (SPKF). The estimation of the helicopter's current state (position, attitude, velocity, and angular velocity) is then fed back in real-time to a state-dependent Riccati equation (SDRE) controller to generate radio control commands to the helicopter. Simulations are provided comparing performance relative to INS/GPS navigation. Experiments also show that an accurate dynamic model of the vehicle is necessary for closed-loop stability. Our results indicate the feasibility of designing a vision-only estimation and control system capable of stabilizing and maneuvering a small unmanned helicopter. Other than simple on-board avionics for low level actuator control, the ground station is responsible for video capture, state-estimation, and state-feedback flight control.

UR - http://www.scopus.com/inward/record.url?scp=34547994465&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547994465&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:34547994465

VL - 2

SP - 1264

EP - 1275

BT - Proceedings of the Institute of Navigation, National Technical Meeting

ER -