This work presents a map management approach for various environments by creating multiple maps with different SLAM (simultaneous localization and mapping) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method, since it is able to automatically trigger new maps (e.g. after the detection of passing a doorway). The appropriate SLAM configuration for the next map is chosen by evaluating laser scan data. Single independent maps are connected by link-points which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loopclosures between maps are detected by an appearance-based method using feature matching and ICP registration between point clouds. The number of possible loop-closure locations is limited to the number of link-points. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purposes and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is also compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing appropriate SLAM configurations for different environmental setups.

This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUKA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in an RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.

This paper presents a voice user interface consisting of several modules for a mobile service robot, which is used to guide people and provide information on a university campus. The recognition and processing system is based on cloud services to convert from speech to text and vice versa and a dialogue system to allow for natural interaction. An approach to combine these modules with a data management system for meal plan, public transit, and location information is presented. We evaluate the system in different environments, each with their individual reverberation times, proving the functionality under conditions typical for the intended use case. In a user study with 13 participants we show the usability of the system, by letting the participants freely interact with the robot. In 86 % of all cases the desired output can be achieved at least once per user and request. A questionnare shows that most users agree with a good usability of the system.

We consider the problem of people search by a mobile social robot in case of a situation that cannot be solved by the robot alone. Examples are physically opening a closed door or operating an elevator. Based on the Behavior Tree framework, we create a modular and easily extendable action sequence with the goal of finding a person to assist the robot. By decomposing the Behavior Tree as a Discrete Time Markov Chain, we obtain an estimate of the probability and rate of success of the options for action, especially where the robot should wait or search for people. In a real-world experiment, the presented method is compared with other common approaches in a total of 588 test runs over the course of one week, starting at two different locations in a university building. We show our method to be superior to other approaches in terms of success rate and duration until a finding person and returning to the start location.

This work presents a non-parametric spatiotemporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multiweek data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.