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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

Flowchart of the proposed EKF-based architecture.
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f5-sensors-12-03162: Flowchart of the proposed EKF-based architecture.

Mentions: Figure 5 depicts an overall flowchart for integrating the camera, IMU and GNSS sensors through the EKF engine. The sensor units are colored cyan while white blocks indicate the utilized algorithms for intermediate parameter calculation. Between camera/GNSS data update, the vehicle motion is estimated by strapdown IMU mechanization. Once new camera/GNSS measurement data arrive, all derived navigation information is transformed in order to enable the EKF engine for accurate navigation solution and IMU sensor calibration. In such a method, the measurement model is straightforward to construct as relatively small/constant size of measurement elements are included in the EKF. The whole processing requires a relatively low computational load since covariance matrices have low dimensionality. Finally, a low-pass filter before implementing the EKF can be added to smooth the camera-derived rotation and translation parameters.


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

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

Flowchart of the proposed EKF-based architecture.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-03162: Flowchart of the proposed EKF-based architecture.
Mentions: Figure 5 depicts an overall flowchart for integrating the camera, IMU and GNSS sensors through the EKF engine. The sensor units are colored cyan while white blocks indicate the utilized algorithms for intermediate parameter calculation. Between camera/GNSS data update, the vehicle motion is estimated by strapdown IMU mechanization. Once new camera/GNSS measurement data arrive, all derived navigation information is transformed in order to enable the EKF engine for accurate navigation solution and IMU sensor calibration. In such a method, the measurement model is straightforward to construct as relatively small/constant size of measurement elements are included in the EKF. The whole processing requires a relatively low computational load since covariance matrices have low dimensionality. Finally, a low-pass filter before implementing the EKF can be added to smooth the camera-derived rotation and translation parameters.

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