From Pixels to Buildings: Endtoend Probabilistic Deep Networks for Largescale Semantic Mapping We introduce TopoNets, endtoend probabilistic deep networks for modeling semantic maps with structure reflecting the topology of largescale environments. TopoNets build a unified deep network spanning multiple levels of abstraction and spatial scales, from pixels representing geometry of local places to highlevel descriptions of semantics of buildings. To this end, TopoNets leverage complex spatial relations expressed in terms of arbitrary, dynamic graphs. We demonstrate how TopoNets can be used to perform endtoend semantic mapping from partial sensory observations and noisy topological relations discovered by a robot exploring largescale office spaces. Thanks to their probabilistic nature and generative properties, TopoNets extend the problem of semantic mapping beyond classification. We show that TopoNets successfully perform uncertain reasoning about yet unexplored space and detect novel and incongruent environment configurations unknown to the robot. Our implementation of TopoNets achieves realtime, tractable and exact inference, which makes these new deep models a promising, practical solution to mobile robot spatial understanding at scale.

Learning Topometric Semantic Maps from Occupancy Grids Todayxe2x80x99s mobile robots are expected to operate in complex environments they share with humans. To allow intuitive humanrobot collaboration, robots require a humanlike understanding of their surroundings in terms of semantically classified instances. In this paper, we propose a new approach for deriving such instancebased semantic maps purely from occupancy grids. We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a randomsized map. The extraction is followed by a postprocessing chain to further increase the accuracy of our approach, as well as place categorization for the three classes room, door and corridor. All detected and classified entities are described as instances specified in a common coordinate system, while a topological map is derived to capture their spatial links. To train our two neural networks used for detection and map segmentation, we contribute a simulator that automatically creates and annotates the required training data. We further provide insight into which features are learned to detect doorways, and how the simulated training data can be augmented to train networks for the direct application on realworld grid maps. We evaluate our approach on several publicly available realworld data sets. Even though the used networks are solely trained on simulated data, our approach demonstrates high robustness and effectiveness in various realworld indoor environments.