Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds
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- Sant R., Kulkarni N., Bakshi A., Kapur S., Goel K. (2013) Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds. In: Chen M., Leibe B., Neumann B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg
Determination of a collision free, optimal path achieved by performing complex computations on an accurate representation of the terrain, is essential to the success of autonomous navigation systems. This paper defines and builds upon a technique – Spatially Important Point (SIP) Identification – for reducing the inherent complexity of range data without discarding any essential information. The SIP representation makes onboard autonomous navigation a viable option, since space and time complexity is greatly reduced. A cost based, dynamic navigation analysis is then performed using only the SIPs which culminates into a new time and space efficient Path Planning technique. The terrain is also retained in the form of a graph, with each branch of every node encountered, indexed in a priority sequence given by its cumulative cost. Experiments show the entire dataflow, from input data to path planning, being done in less than 700ms on a modern computer on datasets typically having 106 points.
KeywordsAutonomous navigation path planning range data dynamic programming spatially important points
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