  Accurate, high-resolution 3D mapping of environmental terrain is critical in
a range of disciplines. In this study, we develop a new technique, called the
PCFilt-94 algorithm, to extract 3D point clouds from coarse resolution
millimetre-wave radar data cubes and quantify their associated uncertainties. A
technique to non-coherently average neighbouring waveforms surrounding each
AVTIS2 range profile was developed in order to reduce speckle and was found to
reduce point cloud uncertainty by 13% at long range and 20% at short range.
Further, a Voronoi-based point cloud outlier removal algorithm was implemented
which iteratively removes outliers in a point cloud until the process converges
to the removal of 0 points. Taken together, the new processing methodology
produces a stable point cloud, which means that: 1) it is repeatable even when
using different point cloud extraction and filtering parameter values during
pre-processing, and 2) is less sensitive to over-filtering through the point
cloud processing workflow. Using an optimal number of Ground Control Points
(GCPs) for georeferencing, which was determined to be 3 at close range (<1.5
km) and 5 at long range (>3 km), point cloud uncertainty was estimated to be
approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian
distribution. These uncertainties are smaller than those reported for other
close-range radar systems used for terrain mapping. The results of this study
should be used as a benchmark for future application of millimetre-wave radar
systems for 3D terrain mapping.
