Challenges in Rapid LIDAR Point Cloud Delivery
LIDAR sensors and LIDAR systems utilized for precise surveying in various fields of application are operated from significantly distinct platforms ? from static platforms during a single 3D scan acquisition in terrestrial or static laser scanning, from a multitude of different platforms in kinematic laser scanning like mobile laser scanning, UAV-based laser scanning or airborne laser scanning. Although these fields of application impose substantially different requirements with respect to accuracy, measurement rate, and data density, serve various data consumer communities and demand vastly dissimilar requirements on the LIDAR equipment, e.g., size, weight, cost and performance, there are some general issues one has to address if data delivery within a very short period of time is mandatory. Rapid ? as in rapid point cloud delivery ? characterizes the time it takes from the actual realtime data acquisition until the delivery of the final point cloud. This time requirement may range from seconds for e.g. object detection in surveillance up to hours, days, or weeks in case of large-scale wide-area surveying campaigns. Point clouds within the context of this paper refer to a clean ? i.e., virtually noise free ? georeferenced and consistent and coherent point cloud. We will discuss general challenges in the data processing chain applicable to all types of LIDAR, regardless of the underlying technology, i.e., waveform LIDAR, discrete LIDAR, single-photon LIDAR or Geiger-mode LIDAR. For example, data acquisition is monitored in many applications closely by an operator to ensure completeness of acquired data by monitoring previews on the registered point cloud more or less simultaneous with acquisition. Another topic is the automated registration or at least pre-registration of all acquired point clouds in a common coordinate system before cleaning the point cloud from noise points originating from either the LIDAR itself, or the environment, or the object?s surface properties.