Odométrie radar par analyse de la distorsion - Applications à la navigation de véhicules terrestres et nautiques

Odométrie radar par analyse de la distorsion

Applications à la navigation de véhicules terrestres et nautiques

Damien Vivet Paul Checchin  Roland Chapuis 

Clermont Université, Université Blaise Pascal, Institut Pascal CNRS, UMR 6602, Institut Pascal BP 10448, F-63171 Aubière

Corresponding Author Email: 
prenom.nom@univ-bpclermont.fr
Page: 
205-228
|
DOI: 
https://doi.org/10.3166/TS.29.205-228
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
31 August 2012
| Citation

OPEN ACCESS

Abstract: 

The use of a rotating range sensor in high speed robotics creates distortions in the collected data. Such an effect is, in the majority of studies, ignored or considered as a noise and then corrected, based on proprioceptive sensors or localization systems. In this study, we consider that distortion contains information about the vehicle’s displacement. We propose to extract this information from distortion without any other information than exteroceptive sensor data. The only sensor used for this work is a panoramic Frequency Modulated Continuous Wave (FMCW) radar called K2Pi. No odometer, gyrometer or other proprioceptive sensor is used. The idea is to resort to velocimetry by analyzing the distortion of the measurements. As a result, the linear and angular velocities of the robot are estimated and used to build, without any other sensor, the trajectory of the vehicle and then the radar map of outdoor environments. Radar-only localization and mapping results are presented for a ground vehicle and a riverbank application. This work can easily be extended to other slow rotating range sensors.

RÉSUMÉ

L’utilisation d’un capteur télémétrique tournant en robotique mobile à haute vitesse implique l’apparition d’une distorsion sur les données collectées. Un tel effet est, dans la majorité des études, ignoré ou considéré comme un bruit et, de ce fait, corrigé en utilisant des capteurs proprioceptifs ou des systèmes de localisation. Dans cet article, la vélocimétrie du véhicule est extraite de la distorsion, sans aucun autre apport que celui fourni par le capteur extéroceptif. Il s’agit d’un radar panoramique FMCW (Frequency Modulated Continuous Wave), appelé K2Pi. Point d’odomètre, de gyromètre ou autre capteur proprioceptif. Les estimations des vitesses angulaires et linéaires du robot sont ensuite utilisées pour construire, sans aucun autre capteur, la trajectoire du véhicule et la carte radar de l’environnement extérieur exploré. Des résultats de localisation et de cartographie avec ce capteur radar, mais facilement extensibles à d’autres capteurs télémétriques tournants, sont présentés pour des véhicules terrestres et nautiques.

Keywords: 

localization, mapping, radar, distortion, odometry

MOTS-CLÉS

localisation, cartographie, radar, distorsion, odométrie

Extended Abstract
1. Introduction
2. Le Phénomène De Distorsion
3. Etat De L’art
4. Le Capteur Radar
5. Analyse De La Distorsion
6. Expérimentations
7. Conclusion
  References

Ait-Aider O., Andreff N., Lavest J.-M., Martinet P. (2006). Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera. In European Conf. on Computer Vision, p. 56-68. Graz, Austria, Springer.

Arras K. O. (2003). Feature-Based Robot Navigation in Known and Unknown Environments. Doctoral dissertation no. 2765, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland.

Borenstein J., Everett H. R., Feng L., Wehe D. (1997). Mobile robot positioning: Sensors and techniques. Journal of Robotic Systems, vol. 14, no 4, p. 231–249.

Checchin P., Gérossier F., Blanc C., Chapuis R., Trassoudaine L. (2009, 7). Radar Scan Matching SLAM using the Fourier-Mellin Transform. In The 7th International Conference on Field and Service Robots (FSR). Cambridge, Massachusetts, USA.

Howard A. (2008). Real-time stereo visual odometry for autonomous ground vehicles. In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems - IROS, p. 3946-3952. Nice, France, IEEE Press.

Jenkin M., Verzijlenberg B., Hogue A. (2010). Progress towards underwater 3D scene recovery. In Proc. of the 3rd Conf. on Computer Science and Software Engineering, p. 123–128. Montréal, Quebec, Canada, ACM.

Julier S., Uhlmann J. (2007). Using covariance intersection for slam. Robotics and Autonomous Systems, vol. 55, no 1, p. 3–20.

Kitt B., Geiger A., Lategahn H. (2010, June). Visual Odometry based on Stereo Image Sequences with RANSAC-based Outlier Rejection Scheme. In IEEE Intelligent Vehicles Symposium. San Diego, USA, IEEE Press.

Kümmerle R., Steder B., Dornhege C., Ruhnke M., et al. (2009). On Measuring the Accuracy of SLAM Algorithms. Journal of Autonomous Robots, vol. 27, no 4, p. 387-407.

Nistér D., Naroditsky O., Bergen J. (2006). Visual odometry for ground vehicle applications. Journal of Field Robotics, vol. 23.

Nüchter A., Lingemann K., Hertzberg J., Surmann H. (2005). Heuristic-Based Laser Scan Matching for Outdoor 6D SLAM. In Advances in Artif. Intellig. 28th German Conf. on AI,p. 304–319. Koblenz, Germany, Springer.

Olson E. (2009). Real-time correlative scan matching. In Proc. Inter. Conf. on Robotics and Automation - ICRA, p. 4387-4393. Kobe, Japan, IEEE Press.

Pretto A., Menegatti E., Bennewitz M., Burgard W., Pagello E. (2009). A Visual Odometry Framework Robust to Motion Blur. In Proc. Inter. Conf. on Robotics and Automation - ICRA, p. 1685–1692. Kobe, Japan, IEEE Press.

Ribas D., Ridao P., Tardós J., Neira J. (2007, October). Underwater SLAM in a Marina Environment. In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems - IROS, p. 1455-1460. San Diego, USA, IEEE Press.

Richardson W. H. (1972, Jan). Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., vol. 62, no 1, p. 55–59. http://www.opticsinfobase.org/abstract.cfm?URI=josa-62-1-55

Rouveure R., Faure P., Monod M. (2010). A New Radar Sensor for Coastal and Riverbank Monitoring. In Observation des Côtes et des Océans : Senseurs et Systèmes (OCOSS 2010). Brest, France.

Thrun S. (2002). Robotic Mapping: A Survey. In G. Lakemeyer, B. Nebel (Eds.), Exploring Artificial Intelligence in the New Millenium. San Francisco, USA, Morgan Kaufmann.

Tipaldi G. D., Ramos F. (2009). Motion clustering and estimation with conditional random fields. In Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p. 872–877. St. Louis, MO, USA, IEEE Press.

Vivet D., Checchin P., Roland C. (2012, janvier). Odométrie radar par analyse de la distorsion - Application à un véhicule roulant à vitesse élevée. In Actes de la conférence RFIA 2012. Lyon, France. http://hal.archives-ouvertes.fr/hal-00656486 (Session "Articles")

Williams B., Reid I. (2010). On Combining Visual SLAM and Visual Odometry. In Proc. Inter. Conf. on Robotics and Automation - ICRA. Anchorage, Alaska, USA, IEEE Press.