Simultaneous Localization And Mapping (SLAM) is one of the main topics investigated in the field of autonomous mobile robots. It permits the Localization and mapping of a robot in a large outdoor environment, using exteroceptive (laser, camera, radar, etc.) and proprioceptive (odometer, gyroscope, etc.) sensors. The objective of this PhD thesis is to develop innovative SLAM that uses a radar frequency modulated continuous wave (FMCW) as an exteroceptive sensor. Microwave radar provides an alternative solution for environmental imaging and overcomes the shortcomings of laser, video and sonar sensors such as their high sensitivity to atmospheric conditions. However, data obtained with this rotating range sensor is adversely affected by the vehicle’s own movement. In order to efficiently manage the work, we propose : a correction, on-the-fly, of the rotating distortion with an algorithm that uses the proprioceptive sensors’ measurements ; development of a new technique for simultaneous localization and mapping named RS-SLAM-FMT ; for the first time in SLAM, the use of the Fourier-Mellin Transform provides an accurate and efficient way of computing the rigid transformation between consecutive scans ; creation of an experimental tool to determine the covariance matrix associated with the observations. It is based on an uncertainty analysis of a Fourier-Mellin image registration ; tests of the robustness of the SLAM algorithm in real-life conditions : in an environment containing a small number of points of interest, in real full speed driving conditions, in peri-urban environments with a high density of moving objects etc. ; creation and experiment of a real-time RS-SLAM-FMT implemented on a mobile exploration vehicle in an extensive outdoor environment.
