A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method. (arXiv:2201.11608v1 [cs.CV])

Web: http://arxiv.org/abs/2201.11608

In this thesis a probabilistic framework is developed and proposed for
Dynamic Object Recognition in 3D Environments. A software package is developed
using C++ and Python in ROS that performs the detection and tracking task.
Furthermore, a novel Gaussian Process Regression (GPR) based method is
developed to detect ground points in different urban scenarios of regular,
sloped and rough. The ground surface behavior is assumed to only demonstrate
local input-dependent smoothness. kernel's length-scales are obtained. Bayesian
inference is implemented sing …

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