Advanced Coincidence Processing of 3D Laser Radar Data

Data collected by 3D Laser Radar (Lidar) systems, which utilize arrays of avalanche photo-diode detectors operating in either Linear or Geiger mode, may include a large number of false detector counts or noise from temporal and spatial clutter. We present an improved algorithm for noise removal and signal detection, called Multiple-Peak Spatial Coincidence Processing (MPSCP). Field data, collected using an airborne Lidar sensor in support of the 2010 Haiti earthquake operations, were used to test the MPSCP algorithm against current state-of-the-art, Maximum A-posteriori Coincidence Processing (MAPCP). Qualitative and quantitative results are presented to determine how well each algorithm removes image noise while preserving signal and reconstructing the best estimate of the underlying 3D scene. The MPSCP algorithm is shown to have 9x improvement in signal-to-noise ratio, a 2-3x improvement in angular and range resolution, a 21% improvement in ground detection and a 5.9x improvement in computational efficiency compared to MAPCP.