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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

Localization results with radar-based odometry. (a) The D-GPS ground truth. (b) In blue, the ground truth; in red, the vehicle localization based on dead reckoning with the proprioceptive sensors; in green, the result with radar-based odometry.
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f16-sensors-13-04527: Localization results with radar-based odometry. (a) The D-GPS ground truth. (b) In blue, the ground truth; in red, the vehicle localization based on dead reckoning with the proprioceptive sensors; in green, the result with radar-based odometry.

Mentions: The trajectory inferred by the velocities and the ground truth trajectory are both presented in Figure 16. One can notice errors when the vehicle is turning at 90° and a divergence of the algorithm at the end of the trajectory (in green) can be seen. The errors in the velocity estimates can be explained by the fast variation of the vehicle's orientation. In such conditions, the assumption of constant velocity required by the algorithm is not respected. Similarly, by default, the radar-odometry algorithm produces sharp curves where the angular velocity changes from −0.2 rad/s to 0.2 rad/s. As a result the algorithm cannot converge.


Localization and mapping using only a rotating FMCW radar sensor.

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

Localization results with radar-based odometry. (a) The D-GPS ground truth. (b) In blue, the ground truth; in red, the vehicle localization based on dead reckoning with the proprioceptive sensors; in green, the result with radar-based odometry.
© Copyright Policy
Related In: Results  -  Collection

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

f16-sensors-13-04527: Localization results with radar-based odometry. (a) The D-GPS ground truth. (b) In blue, the ground truth; in red, the vehicle localization based on dead reckoning with the proprioceptive sensors; in green, the result with radar-based odometry.
Mentions: The trajectory inferred by the velocities and the ground truth trajectory are both presented in Figure 16. One can notice errors when the vehicle is turning at 90° and a divergence of the algorithm at the end of the trajectory (in green) can be seen. The errors in the velocity estimates can be explained by the fast variation of the vehicle's orientation. In such conditions, the assumption of constant velocity required by the algorithm is not respected. Similarly, by default, the radar-odometry algorithm produces sharp curves where the angular velocity changes from −0.2 rad/s to 0.2 rad/s. As a result the algorithm cannot converge.

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