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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