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 then used for path-planning. The ability to work in three-dimensional space allows the usage of autonomous robots in highly structured environments with multiple levels, uneven surface or various elevated and underground crossings. That is important for the usage of robots in real- world scenarios, in urban areas or for disaster rescue missions. Powered by TCPDF (www.tcpdf.org)
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:352438 |
| Date | January 2017 |
| Creators | Škoda, Jan |
| Contributors | Barták, Roman, Obdržálek, David |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
Page generated in 0.0022 seconds