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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

Accelerometer and gyroscope estimation based on the camera/IMU/GNSS integration.
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f11-sensors-12-03162: Accelerometer and gyroscope estimation based on the camera/IMU/GNSS integration.

Mentions: In addition, the IMU sensor bias estimation is shown in Figure 11. Those large biases from both the accelerometer and gyroscope sensors inevitably cause erroneous IMU mechanization solutions in a matter of seconds without sensor calibration. The non-smoothly varying biases, particularly for the accelerometer components, gave rise to the unpredictability of the sensor errors. Simply averaging the sensor biases within even a few seconds may yield an inaccurate navigation solution, particularly when the vehicle dynamics significantly change with velocity and cornering stiffness. As seen in Figure 11, during the vehicle’s cornering, the accelerometer bias on x-axis dramatically dropped by approximately 200 milli-g. However, the actual sensor biases may be, to a certain extent, different from the estimation shown in Figure 11 in terms of the fidelities of the utilized filtering models [16].


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

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

Accelerometer and gyroscope estimation based on the camera/IMU/GNSS integration.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-03162: Accelerometer and gyroscope estimation based on the camera/IMU/GNSS integration.
Mentions: In addition, the IMU sensor bias estimation is shown in Figure 11. Those large biases from both the accelerometer and gyroscope sensors inevitably cause erroneous IMU mechanization solutions in a matter of seconds without sensor calibration. The non-smoothly varying biases, particularly for the accelerometer components, gave rise to the unpredictability of the sensor errors. Simply averaging the sensor biases within even a few seconds may yield an inaccurate navigation solution, particularly when the vehicle dynamics significantly change with velocity and cornering stiffness. As seen in Figure 11, during the vehicle’s cornering, the accelerometer bias on x-axis dramatically dropped by approximately 200 milli-g. However, the actual sensor biases may be, to a certain extent, different from the estimation shown in Figure 11 in terms of the fidelities of the utilized filtering models [16].

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