Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.
Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for automated recognition and classification of spaces into separate semantic regions and use of such information for generation of a topological map of an environment. The association of semantic labels with spatial regions is based on Human Augmented Mapping. The methods presented in this paper are evaluated both in simulation and on real data acquired from an office environment. A major challenge in robotics and artificial intelligence lies in creating robots that are to cooperate with people in human-populated environments, e.g. for domestic assistance or elderly care. Such robots need skills that allow them to interact with the world and the humans living and working therein. In this paper we investigate the question of spatial understanding of human-made environments. The functionalities of our system comprise perception of the world, natural language, learning, and reasoning. For this purpose we integrate state-of-the-art components from different disciplines in AI, robotics and cognitive systems into a mobile robot system. The work focuses on the description of the principles we used for the integration, including cross-modal integration, ontology-based mediation, and multiple levels of abstraction of perception. Finally, we present experiments with the integrated "CoSy Explorer" system and list some of the major lessons that were learned from its design, implementation, and evaluation. In this paper, a method for tracking and following people in indoor environments by using a multi-sensor mobile platform is presented. The general use of the approach is in Human-Augmented Mapping (HAM). Laser range data are employed for people detection by searching for legs. A person following algorithm is then implemented allowing the robot to keep track of and pursue a human user. Results from experiments in a real indoor environment are provided to validate the approach. It is shown that the method can be successfully integrated into interactively controlled mapping and navigation systems. This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chaingraph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space. Service robots designed for domestic settings need to navigate in an environment that they have to share with their users. Thus, they have to be able to report their current state and whereabouts in a way that is comprehensible for the user. Pure metric maps do not usually correspond to the understanding of the environment a user would provide. Thus, the robotic map needs to be integrated with the human representation. This paper describes our framework of Human Augmented Mapping that allows us to achieve this integration. We propose further a method to specify and represent regions that relate to a user's view on the environment. We assume an interactive setup for the specification of regions and show the applicability of our method in terms of distinctiveness for space segmentation and in terms of localisation purposes. Human interaction with a service robot requires a shared representation of the environment for spoken dialogue and task specification where names used for particular locations are depending on personal preferences. A question is how such human oriented models can be tied to the geometric robotic models needed for precise localisation and navigation. We assume that this integration can be based on the information potential users give to a service robot about its working environment. We further believe that this information is best given in an interactive setting (a "guided tour") in this particular environment. This paper presents a pilot study that investigates how humans present a familiar environment to a mobile robot. The study is set up within our concept of Human Augmented Mapping, for which we assume an initial "guided tour" scenario to teach a robot its environment. Results from this pilot study are used to validate a proposed generic environment model for a service robot. 