Sometimes, classical displacement measurements are much more diff

Sometimes, classical displacement measurements are much more difficult and have limitations: inertial sensors are prone to drift, and wheel odometry is unreliable in rough terrain (wheels tend to slip and sink) and as a consequence visual odometric approaches are widely studied [11�C13]. For example, in an underwater or naval environment classical ego-motion techniques are not suitable. In [14], Elkins et al. presented localization system for cooperative boats. In [15], Jenkin et al. proposed an ego-motion technique based on visual SLAM fused with IMU. In order to find displacement with exteroceptive sensors such as range finders, the scan matching method is commonly used [16�C18] but each scan is corrected with proprioceptive sensors especially when the sensor is slow.

In all scan matching work, distortion is taken into account but considered as a disturbance and thus corrected.The only work dealing with distortion as a source of information used a rolling shutter specific camera. In [19], Ait-Aider et al. computed instantaneous 3D pose and velocity of fast moving objects using a single camera image but, in their context, prior knowledge about the observed object is required. In mobile robotics, we have no a priori knowledge about the surrounding environment of the robot. To the best of our knowledge, there is absolutely no work in the field of mobile robotics literature considering distortion as a source of information in an odometric purpose.The originality of this paper 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 mobile ground-based panoramic Frequency Modulated Continuous Wave (FMCW) radar, called IMPALA. Then, the trajectory of the vehicle and the radar map of outdoor environments are built. Localization and mapping results are presented for a ground vehicle application when driving at high speed.Section Entinostat 2 presents the microwave radar scanner developed by a Irstea research team (in the field of agricultural and environmental engineering research) [20]. Section 3 focuses on the analysis of the Doppler effect for velocimetry purpose. Section 4 gives the formulation of the principle used in order to extract information from the distortion.

Finally, Section 5 shows experimental results of this work. Section 6 concludes.2.?The IMPALA RadarThe IMPALA radar was developed by the IRSTEA in Clermont-Ferrand, France, for applications in the environmental monitoring domain and robotics. It is a Linear Frequency Modulated Continuous Wave (LFMCW) radar [21]. The principle of a LFMCW radar consists in transmitting a continuous frequency modulated signal, and measuring the frequency difference (called beat frequency Fb) between the transmitted and the received signals.

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