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Localization and mapping using only a rotating FMCW radar sensor.

Vivet D, Checchin P, Chapuis R - Sensors (Basel) (2013)

Bottom Line: These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems.Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments.In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed.

View Article: PubMed Central - PubMed

Affiliation: Institut Pascal, Université Blaise Pascal, Clermont Université, Clermont-Ferrand, France. damien.vivet@univ-bpclermont.fr

ABSTRACT
Rotating radar sensors are perception systems rarely used in mobile robotics. This paper is concerned with the use of a mobile ground-based panoramic radar sensor which is able to deliver both distance and velocity of multiple targets in its surrounding. The consequence of using such a sensor in high speed robotics is the appearance of both geometric and Doppler velocity distortions in the collected data. These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems. Our purpose is to study and use data distortion and Doppler effect as sources of information in order to estimate the vehicle's displacement. The linear and angular velocities of the mobile robot are estimated by analyzing the distortion of the measurements provided by the panoramic Frequency Modulated Continuous Wave (FMCW) radar, called IMPALA. Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments. In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed.

No MeSH data available.


Related in: MedlinePlus

Trajectory and map reconstruction based on IMPALA radar odometry. (a) Estimated trajectory with IMPALA odometry: in blue the ground truth, in red the reconstructed trajectory with the measurements of the odometer and gyrometer, and in green radar-odometry solution; (b) Aerial view of the experimental area; (c) Map obtained based on radar odometry with the estimated velocities.
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f10-sensors-13-04527: Trajectory and map reconstruction based on IMPALA radar odometry. (a) Estimated trajectory with IMPALA odometry: in blue the ground truth, in red the reconstructed trajectory with the measurements of the odometer and gyrometer, and in green radar-odometry solution; (b) Aerial view of the experimental area; (c) Map obtained based on radar odometry with the estimated velocities.

Mentions: This section provides experimental results of the presented radar-based approach. The IMPALA radar sensor was mounted on a utility car, on top of the vehicle, 2 meters above the ground. The experimental runs that are presented were conducted in an outdoor field, near Clermont-Ferrand in FRANCE, on Blaise Pascal University campus and around the Auvergne Zenith car-park (cf. aerial view in Figures 10(b), 11(b) and 12), with a semistructured environment (buildings, trees, roads, road signs, etc.). Speed estimation has been done on different kinds of displacements, i.e., rectilinear displacement and also classical road traffic displacement with different curves.


Localization and mapping using only a rotating FMCW radar sensor.

Vivet D, Checchin P, Chapuis R - Sensors (Basel) (2013)

Trajectory and map reconstruction based on IMPALA radar odometry. (a) Estimated trajectory with IMPALA odometry: in blue the ground truth, in red the reconstructed trajectory with the measurements of the odometer and gyrometer, and in green radar-odometry solution; (b) Aerial view of the experimental area; (c) Map obtained based on radar odometry with the estimated velocities.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673098&req=5

f10-sensors-13-04527: Trajectory and map reconstruction based on IMPALA radar odometry. (a) Estimated trajectory with IMPALA odometry: in blue the ground truth, in red the reconstructed trajectory with the measurements of the odometer and gyrometer, and in green radar-odometry solution; (b) Aerial view of the experimental area; (c) Map obtained based on radar odometry with the estimated velocities.
Mentions: This section provides experimental results of the presented radar-based approach. The IMPALA radar sensor was mounted on a utility car, on top of the vehicle, 2 meters above the ground. The experimental runs that are presented were conducted in an outdoor field, near Clermont-Ferrand in FRANCE, on Blaise Pascal University campus and around the Auvergne Zenith car-park (cf. aerial view in Figures 10(b), 11(b) and 12), with a semistructured environment (buildings, trees, roads, road signs, etc.). Speed estimation has been done on different kinds of displacements, i.e., rectilinear displacement and also classical road traffic displacement with different curves.

Bottom Line: These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems.Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments.In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed.

View Article: PubMed Central - PubMed

Affiliation: Institut Pascal, Université Blaise Pascal, Clermont Université, Clermont-Ferrand, France. damien.vivet@univ-bpclermont.fr

ABSTRACT
Rotating radar sensors are perception systems rarely used in mobile robotics. This paper is concerned with the use of a mobile ground-based panoramic radar sensor which is able to deliver both distance and velocity of multiple targets in its surrounding. The consequence of using such a sensor in high speed robotics is the appearance of both geometric and Doppler velocity distortions in the collected data. These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems. Our purpose is to study and use data distortion and Doppler effect as sources of information in order to estimate the vehicle's displacement. The linear and angular velocities of the mobile robot are estimated by analyzing the distortion of the measurements provided by the panoramic Frequency Modulated Continuous Wave (FMCW) radar, called IMPALA. Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments. In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed.

No MeSH data available.


Related in: MedlinePlus