Fast gaussian process occupancy maps

Published in ICARCV2018, 2018

Recommended citation: https://arxiv.org/abs/1811.10156.pdf

This work focus on the huge overhead on prediction side of GPOM. By split the local map into three region and merely us GP to predict the middle to tremendously reduce the cost.

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Recommended citation: Yuan, Y., Kuang, H., & Schwertfeger, S. (2018, November). Fast Gaussian Process Occupancy Maps. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1502-1507). IEEE.’