ObjectAware Hybrid Map for Indoor Robot Visual Semantic Navigation * In order to achieve an intuitive interaction and visual semantic navigation for the indoor robot, we propose a novel objectaware hybrid map. The existing map is usually a metric map, lacking semantics for interaction. We combine objects in the indoor environment with the metric map to constitute a hybrid map. The map consists of a 3D object semantic map and a 2D occupancy grid map, which transfers human commands to the grid map through object semantics, thereby enabling autonomous navigation for the robot. We utilize ORBSLAM2 for continuous pose estimation and 3D mapping. 2D object detection in keyframes is conducted based on YOLO v3. The object point clouds in multiple perspectives are merged and a 3D bounding box of the object is estimated. These objects construct a 3D semantic map. Furthermore, we project a 3D point cloud map into a 2D plane in order to get an occupancy grid map. Finally, these two maps are combined forming an objectaware hybrid map. We conduct experiments in real environments in order to verify the feasibility and robustness of the hybrid map for robot semantic navigation.

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.