Our paper on robust navigation using l1-norm low rank approximation is accepted to IROS 2014.

[2014/05/19]

The following paper is accepted to the IEEE International Conference on Intelligent Robots and Systems (IROS 2014):

  • A Robust Autoregressive Gaussian Process Motion Model Using l1-Norm Based Low-Rank Kernel Matrix Approximation by Eunwoo Kim, Sungjoon Choi, and 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 a 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 l1-norm based low-rank kernel approximation, called PCGP-l1. 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 a 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.
  • Video