Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner

Micro aerial vehicles (MAV) pose a challenge in designing sensory systems and algorithms due to their size and weight constraints and limited computing power. We present an efficient 3D multi-resolution map that we use to aggregate measurements from a lightweight continuously rotating laser scanner. We estimate the robot's motion by means of visual odometry and scan registration, aligning consecutive 3D scans with an incrementally built map. By using local multi-resolution, we gain computational efficiency by having a high resolution in the near vicinity of the robot and a lower resolution with increasing distance from the robot, which correlates with the sensor's characteristics in relative distance accuracy and measurement density. Compared to uniform grids, local multi-resolution leads to the use of fewer grid cells without loosing information and consequently results in lower computational costs. We efficiently and accurately register new 3D scans with the map in order to estimate the motion of the MAV and update the map in-flight. In experiments, we demonstrate superior accuracy and efficiency of our registration approach compared to state-of-the-art methods such as GICP. Our approach builds an accurate 3D obstacle map and estimates the vehicle's trajectory in real-time.