This thesis addresses the perception of the environment for the automatic guidance of a mobile robot. When one wishes to achieve autonomous navigation, several elements must be addressed. Among them we will discuss the traversability of the environment on the vehicle path. This traversability depends on the ground geometry and type and also the position of the robot in its environment (in a local coordinate system) taking into acount the objective that must be achieved (in a global coordinate system).The works of this thesis deal with the environment perception of a robot inthe broad sense by addressing the mapping of the environment and the location of the vehicle. To do this, a data fusion system is proposed to estimate these informations. The fusion system is supplied by several low cost sensors including a camera, a rangefinder and a GPS receiver. The originality of this work focuses on how to combine these sensors informations. The base of the fusion process is a visual odometry algorithm based on camera images. To increase the accuracy and the robustness, the initialization of the selected points position is done with a rangefinder that provides the depth information.In addition, the localization in a global reference is made by combining the visual odometry with GPS information. For this, a process has been established to ensure the integrity of localization of the vehicle before merging its position with the GPS data. The mapping of the environment is also important as it will allow to compute the path that will ensure an evolution of the vehicle without risk of collision or overturn. From this perspective, the rangefinder already present in the localization process is used to complete the current list of 3D points that represent the field infront of the vehicle. By combining an accurate localization of the vehicle with informations of the rangefinder it is possible to obtain an accurate, dense and geo-located map environment. All these works have been tested on a robotic simulator developed for this purpose and on a real all-terrain vehicle moving in a natural world. The results of this approach have shown the relevance of this work for autonomous guidance of mobile robots.
