The aim of this paper is to present the potential of microwave radar as a high resolution ground-based imager, in order to build radar maps in environmental applications. A new radar sensor named K2Pi, based on the principle of Frequency-Modulated Continuous Wave (FM-CW) is described. In order to build the radar maps, the R-SLAM algorithm has been developed. It is based on Simultaneous Localization And Mapping (SLAM) principles. The global radar map is constructed through a data merging process, using scan matching of radar image sequences. First results obtained in different environments are presented, which show the ability of the microwave radar to deal with unstructured and extended environments, and to build consistent maps.Index Terms-Environmental mapping, microwave radar, simultaneous localization and mapping.
h i g h l i g h t s• PELICAN is a MMW radar designed for perception in mobile robotics applications. • Use of FMCW modulation for easy implementation and short-range measurements.• Limitation to 2D imaging due to the use of a rotating fan-beam antenna.
a b s t r a c tRobust environmental perception is a crucial parameter for the development of autonomous ground vehicle applications, especially in the field of agricultural robotics which is one of the priorities for the Horizon 2020 robotics funding (EU funding program for research and innovation). Because of uncontrolled and changing environmental conditions in outdoor and natural environments, data from optical sensors classically used in mobile robotics can be compromised and unusable. In such situations, millimeterwave radar can provide an alternative and complementary solution for perception tasks. The aim of this paper is to present the PELICAN radar, a millimeter-wave radar specifically designed for mobile robotics applications, including obstacle detection, mapping and situational awareness in general. In this first of a two-part paper, the choice of a frequency-modulated continuous-wave radar is explained and the theoretical elements of this solution are detailed. PELICAN radar is using a rotating fan-beam antenna, and the construction of 2D representations of the surrounding environments with radar data is described through simulation results. The second part of the paper will be devoted for a detailed description of PELICAN radar, as well as experimental results.
To cite this version:M. Jaud, R. Rouveure, P. Faure, M.O. Monod. Methods for FMCW radar map georeferencing. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2013, 84, p.
AbstractIn a context of mobile environment mapping, a vehicle-based radar system, K2Pi, has been developed. A mapping of the environment is carried out from the radar datasets. Given the specificities of radar maps, the main problem at this stage is to find a method to georeference these maps. This article proposes three radar map georeferencing methods. The first method is a typical manual selection of a set of control point pairs. The second method consists of matching the relative trajectory computed by a specific radar algorithm with a trajectory recorded from absolute DGPS recording. Finally, the third method, inspired by the image-to-image approach, is based on Fourier-Mellin transform which automatically registers the radar map with respect to a georeferenced aerial photograph. Successfully tested on radar datasets, this method could be applied to many other types of data.
The detection and tracking of moving objects (DATMO) in an outdoor environment from a mobile robot are difficult tasks because of the wide variety of dynamic objects. A reliable discrimination of mobile and static detections without any prior knowledge is often conditioned by a good position estimation obtained using Global Positionning System/Differential Global Positioning System (GPS/DGPS), proprioceptive sensors, inertial sensors or even the use of Simultaneous Localization and Mapping (SLAM) algorithms. In this article a solution of the DATMO problem is presented to perform this task using only a microwave radar sensor. Indeed, this sensor provides images of the environment from which Doppler information can be extracted and interpreted in order to obtain not only velocities of detected objects but also the robot's own velocity.
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