Abstrakt
We propose a novel 3D navigation system for autonomous vehicle path-planning. The system processes a point-cloud data from an RGB-D camera and creates a 3D occupancy grid with adaptable cell size.
Occupied grid cells contain normal distribution characterizing the data measured in the area of the cell. The normal distributions are then used for cell classification, traversability, and collision checking.
The space of traversable cells is used for path-planning. The ability to work in three-dimensional space allows autonomous robots to operate in highly structured environments with multiple levels, uneven surfaces, and various elevated and underground crossings.
That is important for the usage of robots in real-world scenarios such as in urban areas and for disaster rescue missions.