Relative pose estimation of a plane on an airfield with automotive-class solid-state LiDAR sensors : Enhancing vehicular localization with point cloud registration

Abstract: Point cloud registration is a technique to align two sets of points with manifold applications across a range of industries. However, due to a lack of adequate sensing technology, this technique has seldom found applications in the automotive sector up to now. With the advent of solid-state Light Detection and Ranging (LiDAR) sensors that are easily integrable in series production vehicles as means to sense the surrounding environment, this technique can be functional to automate their operations. Maneuvering a vehicle in the proximity of a reference object is one such operation, which can only be performed by accurately estimating its position and orientation relative to the vehicle itself. This project deals with the design and the implementation of an algorithm to accurately locate an aircraft parked on an airfield apron in real time. This is achieved by registering the point cloud model of the plane to the measurement point cloud of the scene produced by the LiDAR sensors on board the vehicle. To this end, the Iterative Closest Point (ICP) algorithm is a well-established approach to register two sets of points without prior knowledge of the correspondences between pairs of points, which, however, is notoriously sensitive towards outliers and computationally expensive with large point clouds. In this work, different variants are presented that improve on the standard ICP algorithm, in terms of accuracy and runtime performance, by leveraging different data structures to index the reference model and outlier rejection strategies. The results show that the implemented algorithms can produce estimates of centimeter precision in milliseconds based only on partial observations of the aircraft, outperforming another established solution tested.