This paper is about the number 1. The number 2 is left for future work.
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
In this project, I will attempt to make fully autonomous of our small rescue robot on various terrain. It will consist of localisation(6DoF), planning and execution(flippers & wheels).
This is the project during my research visiting at Andread Nuechter’s group. The goal is to build efficient feature for very large point cloud matching, e.g., the city data, which is a challenge due to the large size of data and the weak capability of recent descriptor in such a dataset.
The problem want to solve is to generate the topological map incrementally during the investigation of robot such that the motion planning can benefit from it.