Fast gaussian process occupancy maps
Published in ICARCV2018, 2009
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. 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.
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.’