Limits...
Monocular camera/IMU/GNSS integration for ground vehicle navigation in challenging GNSS environments.

Chu T, Guo N, Backén S, Akos D - Sensors (Basel) (2012)

Bottom Line: As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time.Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment.The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.

View Article: PubMed Central - PubMed

Affiliation: School of Earth and Space Sciences, Peking University, Haidian District, Beijing, China. tianxing.chu@colorado.edu

ABSTRACT
Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.

No MeSH data available.


Related in: MedlinePlus

Inertial frame, earth frame and navigation frame representation on the Earth ellipsoid.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376587&req=5

f1-sensors-12-03162: Inertial frame, earth frame and navigation frame representation on the Earth ellipsoid.

Mentions: Figure 1 illustrates three major coordinate frames on the Earth ellipsoid. The inertial coordinate frame (i frame) O-XiYiZi is the fundamental reference frame for inertial navigation and an IMU enclosure usually measures the specific force as well as the angular rate relative to the i frame. Its origin is on the Earth’s center of mass O and it is stationary relative to the fixed stars. We usually express the ultimate position solution, latitude φ, longitude λ and height h, in the Earth frame (e frame) O-XeYeZe. Its origin is the Earth’s center of mass O, with Ze axis forming along polar axis and Xe axis lying on the intersection of the equatorial plane and prime meridian plane. The Ye axis is also on the equatorial plane and satisfies the right-hand rule. The origin and Z-axes of the i and e frames are coincident, respectively. In this paper, the e frame-based position solutions are based on the WGS-84 ellipsoid. To describe the velocity as well as the orientation of a ground vehicle, the right-handed navigation frame (n frame) P-XnYnZn is utilized and locally placed at the user’s position P. The Zn axis is collinear with the local normal line of the reference ellipsoid pointing downwards while Xn and Yn axes indicate the local north and east direction, respectively.


Monocular camera/IMU/GNSS integration for ground vehicle navigation in challenging GNSS environments.

Chu T, Guo N, Backén S, Akos D - Sensors (Basel) (2012)

Inertial frame, earth frame and navigation frame representation on the Earth ellipsoid.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376587&req=5

f1-sensors-12-03162: Inertial frame, earth frame and navigation frame representation on the Earth ellipsoid.
Mentions: Figure 1 illustrates three major coordinate frames on the Earth ellipsoid. The inertial coordinate frame (i frame) O-XiYiZi is the fundamental reference frame for inertial navigation and an IMU enclosure usually measures the specific force as well as the angular rate relative to the i frame. Its origin is on the Earth’s center of mass O and it is stationary relative to the fixed stars. We usually express the ultimate position solution, latitude φ, longitude λ and height h, in the Earth frame (e frame) O-XeYeZe. Its origin is the Earth’s center of mass O, with Ze axis forming along polar axis and Xe axis lying on the intersection of the equatorial plane and prime meridian plane. The Ye axis is also on the equatorial plane and satisfies the right-hand rule. The origin and Z-axes of the i and e frames are coincident, respectively. In this paper, the e frame-based position solutions are based on the WGS-84 ellipsoid. To describe the velocity as well as the orientation of a ground vehicle, the right-handed navigation frame (n frame) P-XnYnZn is utilized and locally placed at the user’s position P. The Zn axis is collinear with the local normal line of the reference ellipsoid pointing downwards while Xn and Yn axes indicate the local north and east direction, respectively.

Bottom Line: As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time.Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment.The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.

View Article: PubMed Central - PubMed

Affiliation: School of Earth and Space Sciences, Peking University, Haidian District, Beijing, China. tianxing.chu@colorado.edu

ABSTRACT
Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.

No MeSH data available.


Related in: MedlinePlus