A Gaussian Process-Based Ground Segmentation for Sloped Terrains

This paper proposes a 3D point cloud registration method based on light detection and ranging (LiDAR) system. The proposed method consists of three steps: Gaussian-Process based ground segmentation, a novel k-neighbors based dynamic point feature and Iterative Closest Point (ICP) fine registration. The first two steps are the preparation of ICP fine registration. The odometry information from a GPS/IMU system is used to compensate the vehicle's ego-motion. The Gaussian-Process based ground segmentation is adopted to remove ground points. A novel Initial Localization based Dynamic Feature (ILDF) is proposed to detect and remove dynamic points. It is applicable in sequential frames and a proper initial localization without a large dislocation. In experiment results, a large number of dynamic points will be detected and removed by ILDF. The removal of dynamic points improves both accuracy and efficiency of registration algorithm.

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Robust Ground Segmentation for Autonomous Driving Using Sparse Gaussian Process Regression

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.10.201 ◽

2021 ◽

Vol 54 (10) ◽

pp. 437-442

Author(s):

Xianjian Jin ◽

Hang Yang ◽

Zeyuan Yan ◽

Qikang Wang

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Autonomous Driving ◽

Ground Segmentation

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Sparse Gaussian process regression based ground segmentation for autonomous land vehicles

The 27th Chinese Control and Decision Conference (2015 CCDC) ◽

10.1109/ccdc.2015.7162621 ◽

2015 ◽

Cited By ~ 1

Author(s):

Tongtong Chen ◽

Bin Dai ◽

Daxue Liu ◽

Jinze Song

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Land Vehicles ◽

Ground Segmentation

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Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

Journal of Applied Probability ◽

10.1017/s0021900200003041 ◽

2007 ◽

Vol 44 (02) ◽

pp. 393-408 ◽

Cited By ~ 1

Author(s):

Allan Sly

Keyword(s):

Stochastic Process ◽

Brownian Motion ◽

White Noise ◽

Gaussian Process ◽

Discrete Data ◽

Hölder Exponent ◽

Scaling Properties ◽

Multifractional Brownian Motion ◽

Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

Methods of Information in Medicine ◽

10.1055/s-0038-1636689 ◽

1987 ◽

Vol 26 (03) ◽

pp. 117-123

Author(s):

P. Tautu ◽

G. Wagner

Keyword(s):

Stochastic Model ◽

Gaussian Process ◽

Covariance Function ◽

Neoplastic Disease ◽

Disease Phenotype ◽

Probabilistic Representation ◽

Temporal Behaviour ◽

Continuous Trait ◽

Continuous Parameter ◽

Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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