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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

Number of matched features after implementing multilayer RANSAC.
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f3-sensors-12-03162: Number of matched features after implementing multilayer RANSAC.

Mentions: To test the multilayer RANSAC scheme, we use a sequence of images (2.5 Hz rate) which will be elaborated in Section 7. Figure 3 shows the number of originally matched features as well as that after implementing multilayer RANSAC. It is clearly seen that after the fourth layer of RANSAC processing, there are still hundreds of matched features for every moment to maintain observable redundancy for motion estimation.


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

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

Number of matched features after implementing multilayer RANSAC.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-03162: Number of matched features after implementing multilayer RANSAC.
Mentions: To test the multilayer RANSAC scheme, we use a sequence of images (2.5 Hz rate) which will be elaborated in Section 7. Figure 3 shows the number of originally matched features as well as that after implementing multilayer RANSAC. It is clearly seen that after the fourth layer of RANSAC processing, there are still hundreds of matched features for every moment to maintain observable redundancy for motion estimation.

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