A point cloud can provide a detailed three dimensional (3D) description of a scene. Partitioning of a point cloud into semantic classes is important for scene understanding, which can be used in autonomous navigation for unmanned vehicles and in applications including surveillance, mapping, and reconnaissance. In this paper, we give a review of recent machine learning techniques for semantic segmentation of point clouds from scanning lidars and an overview of model compression techniques. We focus especially on scan-based learning approaches, which operate on single sensor sweeps. These methods do not require data registration and are suitable for real-time applications. We demonstrate how these semantic segmentation techniques can be used in defence applications in surveillance or mapping scenarios with a scanning lidar mounted on a small UAV.
Currently there is a considerable development of small, lightweight, lidar systems, for applications in autonomous cars. The development gives possibilities to equip small UAVs with this type of sensor. Adding an active sensor component, beside the more common passive UAV sensors, can give additional capabilities. This paper gives experimental examples of lidar data and discusses applications and capabilities for the platform and sensor concept including the combination with data from other sensors. The lidar can be used for accurate 3D measurements and has a potential for detection of partly occluded objects. Additionally, positioning of the UAV can be obtained by a combination of lidar data and data from other low-cost sensors (such as inertial measurement units). The capabilities are attainable both for indoor and outdoor shortrange applications.
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