Simultaneous Localization And Mapping (SLAM) algorithms based on point cloud registration have proven effective in mobile robotics over the last decades. However, they are susceptible to failure when a robot sustains extreme velocities and accelerations. For example, this kind of motion can happen after a collision, causing lidar scans to be heavily skewed. While point cloud de-skewing methods have been explored in the past to increase localization and mapping accuracy, these methods still rely on highly accurate odometry systems or ideal navigation conditions. In this presentation, a new point cloud registration algorithm taking into account the uncertainty left after de-skewing a point cloud will be presented and its performance in a SLAM algorithm will be analyzed.

The presentation will be given in English and the slides will be in English.