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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

Representation of the camera frame with respect to the pinhole projection model.
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f2-sensors-12-03162: Representation of the camera frame with respect to the pinhole projection model.

Mentions: The specific force and angular rate from an IMU are usually measured relative to the i frame and expressed in the body frame (b frame). The b frame has its origin at the center of the IMU enclosure. If the IMU is mounted parallel to the vehicle frame, we allocate the Xb axis pointing forward, in view of the vehicle, and Zb axis aligning with local gravity direction. The Yb axis satisfies the right-hand rule and indicates the right-side direction of the vehicle. Based on the pinhole projection model in our research, the image feature points are expressed in the camera frame (c frame) which is extended from the 2D image plane. The c frame representation is shown in Figure 2. The c frame’s origin is at the camera center C, and Xc as well as Yc axes point towards left and upward directions of the image plane, correspondingly. The Zc axis lies along the principal axis and is orthogonal to the image plane. f denotes the focal length of the camera.


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

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

Representation of the camera frame with respect to the pinhole projection model.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-03162: Representation of the camera frame with respect to the pinhole projection model.
Mentions: The specific force and angular rate from an IMU are usually measured relative to the i frame and expressed in the body frame (b frame). The b frame has its origin at the center of the IMU enclosure. If the IMU is mounted parallel to the vehicle frame, we allocate the Xb axis pointing forward, in view of the vehicle, and Zb axis aligning with local gravity direction. The Yb axis satisfies the right-hand rule and indicates the right-side direction of the vehicle. Based on the pinhole projection model in our research, the image feature points are expressed in the camera frame (c frame) which is extended from the 2D image plane. The c frame representation is shown in Figure 2. The c frame’s origin is at the camera center C, and Xc as well as Yc axes point towards left and upward directions of the image plane, correspondingly. The Zc axis lies along the principal axis and is orthogonal to the image plane. f denotes the focal length of the camera.

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