Efficient LiDAR-Based Object Segmentation and Mapping for Maritime Environments

Abstract

This paper proposes a method that utilizes a 3-D occupancy grid to efficiently map a large area while retaining simple representations of objects for path planning and provide spatial characteristics of objects, which may be used for object classification. To enable large-scale mapping of objects, a region around the unmanned surface vehicle (USV) is defined where a high density of LiDAR returns is expected, termed the visibility horizon. The polygon intersection between the visibility horizon and the newly detected objects is computed, as well as the polygon subtraction of the visibility horizon from the mapped list of polygons. The two polygon lists are then combined using a polygon union operation, with the objects retaining class designations. The result is a 2-D map that contains polygon representations of objects, where the object is described with a tunable number of vertices and may have an associated object class. Thus, providing necessary information for path planning and tasking. The resultant polygons are shown here to be accurate to 20 cm using a 10-cm occupancy grid and 16-ft-long unmanned surface vehicles with four multibeam LiDAR sensors.