Extracting Proprioceptive Information By Analyzing Rotating Range Sensors Induced Distortion

The increased autonomy of robots is directly linked to their capability to perceive their environment. Simultaneous Localization and Mapping (SLAM) techniques, which associate perception and movement, are particularly interesting because they provide advanced autonomy to vehicles in the field of Intelligent Transportation Systems (ITS). Such ITS are based on both proprioceptive sensors to estimate their dynamics and exteroceptive sensors in order to perceive the surrounding of the vehicle. This second class of sensor is dominated by camera and rotating range sensors such as LIDAR or RADAR. Indeed, the majority of intelligent vehicles uses today 2D/3D laser or panoramic radar to localize itself or detect and avoid obstacles. The use of a rotating range sensor, while moving at high speed, creates distortions in the collected data. Such an effect is, in the majority of studies, ignored or considered as noise and then corrected, based on additional proprioceptive sensors or localization systems. In this study, rather than considering distortion as a noise, we consider that it contains all the information about the vehicle’s displacement. We propose to extract this information from such distortion without any other information than the exteroceptive sensor data. The idea is to resort to velocimetry by only analyzing the distortion of the measurements. As a result, we propose a linear and angular velocities estimator of the mobile robot based on the distortion analysis