learning semantic maps with topological spatial relations...
Post on 03-Aug-2020
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Learning Semantic Mapswith Topological Spatial Relations Using
Graph-Structured Sum-Product Networks
Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao
University of Washington
• Real-world graph-structured data:
• Complex
• Noisy
• Dynamic (with varying size)
• Example: Semantic Topological Maps
•Traditional structured prediction approaches:•Place strict constraints on variable interactions•Require fixed number of variables•Require static global structure
Motivation
• Graph-Structured Sum-Product Networks (GraphSPNs):
• GraphSPNs learn models of global semantic maps with topological spatial relations
Deep probabilisticmodel for SP
Complex, noisy variable
dependencies
Template models dynamic graphs of varying size
Disambiguate local uncertain
semantic information
Infer semanticsof unexplored
places
Detect novel environment
structure
Contributions
Experimental Results
Semantic Categories
NoisifiedGroundtruth Inferred
GraphSPN
MRF
P(Semantics)
Robust Probabilistic Inference
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