NDLI: A robust autoregressive gaussian process motion model using l1-norm based low-rank kernel matrix approximation

Content Provider	IEEE Xplore Digital Library
Author	Eunwoo Kim Sungjoon Choi Songhwai Oh
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Seoul Nat. Univ., Seoul, South Korea (Eunwoo Kim; Sungjoon Choi; Songhwai Oh)
Abstract	This paper considers the problem of modeling complex motions of pedestrians in a crowded environment. A number of methods have been proposed to predict the motion of a pedestrian or an object. However, it is still difficult to make a good prediction due to challenges, such as the complexity of pedestrian motions and outliers in a training set. This paper addresses these issues by proposing a robust autoregressive motion model based on Gaussian process regression using $l_{1}-norm$ based low-rank kernel matrix approximation, called $PCGP-l_{1}.$ The proposed method approximates a kernel matrix assuming that the kernel matrix can be well represented using a small number of dominating principal components, eliminating erroneous data. The proposed motion model is robust against outliers present in a training set and can reliably predict the motion of a pedestrian, such that it can be used by a robot for safe navigation in a crowded environment. The proposed method is applied to a number of regression and motion prediction problems to demonstrate its robustness and efficiency. The experimental results show that the proposed method considerably improves the motion prediction rate compared to other Gaussian process regression methods.
Starting Page	4396
Ending Page	4401
File Size	2762652
Page Count	6
File Format	PDF
e-ISBN	9781479969340
DOI	10.1109/IROS.2014.6943184
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Approximation methods Gaussian processes Robustness Trajectory Robots Vectors
Content Type	Text
Resource Type	Article

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