Recursive prediction algorithm for non-stationary Gaussian Process

Zhang, Yulai; Luo, Guangsheng

doi:10.1016/j.jss.2016.08.036

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…A constant length-scale may not be used with LIDAR data due to non-smoothness of collected point cloud. Different methods are proposed to adjust length scales locally for non-stationary covariance functions by assuming an exact functional relationship for length-scale values [13], [14], [15]. The ground segmentation method proposed by [8] assumes the length-scales to be a defined function of line features in different segments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation

Mehrabi¹,

Taghirad²

2021

Preprint

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Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel GPbased method is proposed for the ground segmentation task in rough driving scenarios. The non-stationary covariance function proposed by [1] is utilized as the kernel for the GP. The ground surface behavior is assumed to only demonstrate local-smoothness. Thus, point estimates of the kernel's length-scales are obtained. Thus, two Gaussian processes are introduced to separately model the observation and local characteristics of the data. While, the observation process is used to model the ground, the latent process is put on lengthscale values to estimate point values of length-scales at each input location. Input locations for this latent process are chosen in a physically-motivated procedure to represent an intuition about ground condition. Furthermore, an intuitive guess of length-scale value is represented by assuming the existence of hypothetical surfaces in the environment that every bunch of data points may be assumed to be resulted from measurements from this surfaces. Bayesian inference is implemented using maximum a Posteriori criterion. The log-marginal likelihood function is assumed to be a multitask objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results shows the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process based ground segmentation methods. While adjacent segments do not have similar ground structure in an uneven scene, the proposed method gives an efficient ground estimation based on a whole-frame viewpoint instead of just estimating segmentwise probable ground surfaces.

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Section: Introductionmentioning

confidence: 99%

A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation

Mehrabi¹,

Taghirad²

2021

Preprint

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“…A constant length scale may not be used with LiDAR data due to the nonsmoothness of the collected point cloud. Different methods are proposed to adjust length scales locally for non-stationary covariance functions by assuming an exact functional relationship for length-scale values [14]- [16]. The ground segmentation method proposed by [6] assumes the length scales to be a defined function of line features in different segments.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process-Based Ground Segmentation for Sloped Terrains

Mehrabi¹,

Taghirad²

2021

Preprint

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A Gaussian Process (GP) based ground segmentation method is proposed in this paper which is fully developed in a probabilistic framework. The proposed method tends to obtain a continuous realistic model of the ground. The LiDAR three-dimensional point cloud data is used as the sole source of the input data. The physical realities of the data are taken into account to properly classify sloped ground as well as the flat ones. Furthermore, unlike conventional ground segmentation methods, no height or distance constraints or limitations are required for the algorithm to be applied to take all the regarding physical behavior of the ground into account. Furthermore, a density-like parameter is defined to handle ground-like obstacle points in the ground candidate set. The non-stationary covariance kernel function is used for the Gaussian Process, by which Bayesian inference is applied using the maximum A Posteriori criterion. The log-marginal likelihood function is assumed to be a multitask objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results show the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process-based ground segmentation methods.

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“…Thus, the estimation methods for Wiener systems can be employed to address this identification problem [18–20]. One of the main techniques is the recursive prediction error method, which solves the estimation problem by using gradient‐based approximation techniques [21, 22]. For example, Wigren presented a novel recursive prediction error algorithm for non‐linear Wiener systems by introducing an approximation of the quantiser [23].…”

Section: Introductionmentioning

confidence: 99%