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

Sky plot of available GPS and GLONASS satellites with the elevation mask angle of 40 degree.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-03162: Sky plot of available GPS and GLONASS satellites with the elevation mask angle of 40 degree.

Mentions: As described in Section 5, the scale factor ambiguity between every two successive image frames can be resolved using GNSS differential techniques, capable of obtaining the distance traveled over ground. In an effort to acquire more available GNSS measurements in potentially challenging environments, we take advantage of both GPS and GLONASS constellations for the pseudorange simulation. In typical challenging GNSS environments like urban downtown areas, low-elevation-angle signals are prone to severe multipath degradation or obstruction caused by nearby buildings. We, therefore, filter out those low-elevation-angle satellites from subsequent integration demonstrations. Figure 6 shows the sky plot of GPS/GLONASS satellites when the dataset was initially collected in Málaga with the elevation mask angle of 40 degree. The GPS pseudo random number (PRN) is between 1 and 32, while GLONASS orbital slot is designated within the range 51 to 74. Although the elevation mask angle is significantly higher than in most other applications, both constellations contributed 8 space vehicles for the integration processing. According to the differential operation described in Equation (15), most of the common error sources can be eliminated from the satellite-to-user geometry.


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

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

Sky plot of available GPS and GLONASS satellites with the elevation mask angle of 40 degree.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-03162: Sky plot of available GPS and GLONASS satellites with the elevation mask angle of 40 degree.
Mentions: As described in Section 5, the scale factor ambiguity between every two successive image frames can be resolved using GNSS differential techniques, capable of obtaining the distance traveled over ground. In an effort to acquire more available GNSS measurements in potentially challenging environments, we take advantage of both GPS and GLONASS constellations for the pseudorange simulation. In typical challenging GNSS environments like urban downtown areas, low-elevation-angle signals are prone to severe multipath degradation or obstruction caused by nearby buildings. We, therefore, filter out those low-elevation-angle satellites from subsequent integration demonstrations. Figure 6 shows the sky plot of GPS/GLONASS satellites when the dataset was initially collected in Málaga with the elevation mask angle of 40 degree. The GPS pseudo random number (PRN) is between 1 and 32, while GLONASS orbital slot is designated within the range 51 to 74. Although the elevation mask angle is significantly higher than in most other applications, both constellations contributed 8 space vehicles for the integration processing. According to the differential operation described in Equation (15), most of the common error sources can be eliminated from the satellite-to-user geometry.

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