Semantic Object Maps for Robotic Housework - Representation, Acquisition and Use
Acquiring Semantic Maps for Household Tasks
In this paper, we extend our previous semantic mapping methods with means to acquire models that extend the information content of semantic maps such that they can answer the following categories of queries: “What do parts of the kitchen look like?”, “How can a container be opened and closed?”, “Where do objects of daily use belong?”, “What is inside of cupboards/drawers?”, etc. These are the kinds of information that are required for fetch and delivery applications in the household domain or factory domain respectively. Besides the information content of the environment models, the research presented in this paper also advances the mechanisms for acquiring such semantic maps substantially. Instead of acquiring the maps with a more accurate but slower tilting laser scanner, we use the inexpensive but more limited Kinect RGBD sensor that allows for much faster environment model acquisition and enables the acquisition of visual environment representations. We also generalized the perception methods, including handle detection and recognition, such that they are not specific to particular environments. Paper is under submission and available on demand. [ video ] | ![]() ![]() |
Autonomous Semantic Mapping for Robots
Performing Everyday Manipulation Tasks in Kitchen Environments
In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot’s proprioceptive capabilities and is carried out through the robot’s interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map’s applicability for the task at hand (e.g. resolving counter candidates by our knowledge processing system). [ pdf ] [ video ] | ![]() ![]() |