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Improved stochastic detection algorithms with applications in radar and Ladar based Robotic Mapping

Doctor en Ingeniería Eléctrica / In this work a set of robust tools for noise reduction and feature detection in radar and ladar
environment data for outdoor robotic applications have been developed.
One of these tools is a new noise reduction technique for radar data which combines the
well known spectral noise subtraction with the standard constant false alarm rate (CFAR)
detectors and binary integration. The resulting method exhibited a relatively low time complexity
compared to other state-of-the art noise reduction techniques, the Wiener filter and
Wavelet denoising, while retaining a higher Signal-to-Noise ratio. The method was tested
with real data from a local park captured with a scanning radar mounted on a robot platform
and with SAR data available from NASA/JPL UAVSAR missions.
The second component of this framework is the extension of the standard CFAR detectors
used in radar data to be used with 3D ladar point cloud data. More generally, these extensions
can be used with any kind of 3D point cloud data which comply with the stochastic CFAR
assumptions. The extended CFAR detectors are capable of processing a 2D projected version
of the 3D data or they can work directly on the 3D point cloud. The main modifications to
the original methods include making the CFAR window size parameter an adaptive one and
adding the capability to work with sparse data, in contrast to dense data which is what the
original methods expect. The extended CFAR detectors show a more robust performance
than other methods when the point cloud data contains high noise and clutter rates. The
output of these detector applied to ladar 3D data could then be used for algorithms requiring
high accuracy in the detection, for instance, the Iterative Closest Point (ICP) registration
method. / Este trabajo ha sido parcialmente financiado por el Programa de Becas para estudios de Doctorado año 2010 de CONOCYT
Date January 2018
CreatorsLühr Sierra, Daniel Vicente
ContributorsAdams, Martin, Ruiz del Solar, Javier, Torres Torriti, Miguel, Clark, Daniel
PublisherUniversidad de Chile
Source SetsUniversidad de Chile
Detected LanguageEnglish
RightsAttribution-NonCommercial-NoDerivs 3.0 Chile,

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