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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

IMPALA odometry results in dynamic environment. (a) Linear velocity estimation: in blue the ground truth plotted on the two graphs, in green the Doppler estimation and in red the odometry estimation, both plotted with their respective uncertainty; (b) Angular velocity estimation (in red) along with ground truth (c) Estimated trajectories: in red the IMPALA odometry and in blue the ground truth; (d) Map obtained based on the trajectory solution.
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f21-sensors-13-04527: IMPALA odometry results in dynamic environment. (a) Linear velocity estimation: in blue the ground truth plotted on the two graphs, in green the Doppler estimation and in red the odometry estimation, both plotted with their respective uncertainty; (b) Angular velocity estimation (in red) along with ground truth (c) Estimated trajectories: in red the IMPALA odometry and in blue the ground truth; (d) Map obtained based on the trajectory solution.

Mentions: Ego-motion results with the IMPALA radar.


Localization and mapping using only a rotating FMCW radar sensor.

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

IMPALA odometry results in dynamic environment. (a) Linear velocity estimation: in blue the ground truth plotted on the two graphs, in green the Doppler estimation and in red the odometry estimation, both plotted with their respective uncertainty; (b) Angular velocity estimation (in red) along with ground truth (c) Estimated trajectories: in red the IMPALA odometry and in blue the ground truth; (d) Map obtained based on the trajectory solution.
© Copyright Policy
Related In: Results  -  Collection

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

f21-sensors-13-04527: IMPALA odometry results in dynamic environment. (a) Linear velocity estimation: in blue the ground truth plotted on the two graphs, in green the Doppler estimation and in red the odometry estimation, both plotted with their respective uncertainty; (b) Angular velocity estimation (in red) along with ground truth (c) Estimated trajectories: in red the IMPALA odometry and in blue the ground truth; (d) Map obtained based on the trajectory solution.
Mentions: Ego-motion results with the IMPALA radar.

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