Sensors 2018, 18(2), 582; https://doi.org/10.3390/s18020582
Structured Kernel Subspace Learning for Autonomous Robot Navigation†
Department of Electrical and Computer Engineering and ASRI, Seoul National University, Seoul 08826, Korea
†
This paper is an extended version of our paper published in the 2015 IEEE International Conference on Robotics and Automation (ICRA).
*
Author to whom correspondence should be addressed.
Received: 9 January 2018 / Revised: 6 February 2018 / Accepted: 12 February 2018 / Published: 14 February 2018
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
Abstract
This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm andKeywords:
kernel subspace learning; low-rank approximation; Gaussian processes; motion prediction; motion control
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Kim, E.; Choi, S.; Oh, S. Structured Kernel Subspace Learning for Autonomous Robot Navigation. Sensors 2018, 18, 582.
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