MultiLayer Environmental Affordance Map for Robust Indoor Localization, Event Detection and Social Friendly Navigation In this paper, we propose a novel system architecture called multilayer environmental affordance map for social and service companion robots. Based on this architecture, robots can organize the perception and inference information efficiently and generate social friendly navigation strategies. In other words, robots are able to strengthen their perception and inference abilities to interact with domestic environment and users under our efficient framework. The main feature of this architecture is that the relations between layers can be viewed as affordances to improve the accuracy and the robustness of the detection and inference. The results show that our architecture achieves robust indoor localization, scene localization, human event detection and socially friendly navigation in real time under limited computational resource.

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.