The increasing number of robots in home environments leads to an emerging coexistence between humans and robots. Robots undertake common tasks and support the residents in their everyday life. People appreciate the presence of robots in their environment as long as they keep the control over them. One important aspect is the control of a robot's workspace. Therefore, we introduce virtual borders to precisely and flexibly define the workspace of mobile robots. First, we propose a novel framework that allows a person to interactively restrict a mobile robot's workspace. To show the validity of this framework, a concrete implementation based on visual markers is implemented. Afterwards, the mobile robot is capable of performing its tasks while respecting the new virtual borders. The approach is accurate, flexible and less time consuming than explicit robot programming. Hence, even non-experts are able to teach virtual borders to their robots which is especially interesting in domains like vacuuming or service robots in home environments.
In contrast to mapping physical environments, virtual borders have to be mapped in a different way. @cite_30 propose a stroke-based interface on a tablet PC to control the workspace of a vacuum cleaning robot. This technique needs several top-down view cameras in the robot's environment to cover the whole working area. Commercial solutions to the problem of defining the robot's workspace comprise magnetic strips that are placed on the ground to indicate the borders @cite_24 or beacon devices to generate infrared signals @cite_1 . These conic beams will not be crossed by the robot and can be used to block doors or corridors. The disadvantages are intrusiveness, low flexibility and energy consumption. Other explicit methods to incorporate additional information into maps encompass situated dialogues @cite_11 or activity recognition based on wearables @cite_28 . These works integrate semantics into maps, but they do not allow the flexible definition of certain areas, e.g. arbitrary polygons inside a room. The flexibility of our approach distinguishes our work from previous works.
@cite_30 @cite_28 @cite_1 @cite_24 @cite_11
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We introduce a new approach for exploration based on the concept of frontiers, regions on the boundary between open space and unexplored space. By moving to new frontiers, a mobile robot can extend its map into new territory until the entire environment has been explored. We describe a method for detecting frontiers in evidence grids and navigating to these frontiers. We also introduce a technique for minimizing specular reflections in evidence grids using laser-limited sonar. We have tested this approach with a real mobile robot, exploring real-world office environments cluttered with a variety of obstacles. An advantage of our approach is its ability to explore both large open spaces and narrow cluttered spaces, with walls and obstacles in arbitrary orientation. For mobile robots to operate in compliance with human presence, interpreting the impact of human activities and responding constructively is a challenging goal. In this paper, we propose a generative approach for enhancing robot mapping and mobility in the presence of humans through a joint, probabilistic treatment of static and dynamic characteristics of indoor environments. Human spatial activity is explicitly exploited for the purpose of passage detection and space occupancy prediction while effectively discarding false positive human detections using prior map information. In turn, this allows the execution of plan trajectories within unexplored areas by using human presence for resolving the uncertainty or ambiguity that is due to dynamic events. A series of experiments with an indoor robot navigating in close human proximity within a multi-floor building demonstrate the effectiveness of our approach in realistic conditions. This thesis describes a scalable robotic navigation system that builds a map of the robot's environment on the fly. This problem is also known as Simultaneous Localization and Mapping (SLAM). The SLAM problem has as inputs the control of the robot's motion and sensor measurements to features in the environment. The desired output is the path traversed by the robot (localization) and a representation of the sensed environment (mapping). The principal contribution of this thesis is the introduction of a framework, termed Atlas, that alleviates the computational restrictions of previous approaches to SLAM when mapping extended environments. The Atlas framework partitions the SLAM problem into a graph of submaps, each with its own coordinate system. Furthermore, the framework facilitates the modularity of sensors, map representations, and local navigation algorithms by encapsulating the implementation specific algorithms module that matches submaps and a verification procedure that trades latency in loop closing with a lower chance of incorrect loop detections inherent with symmetric environments. The framework is demonstrated with several datasets that map large indoor and urban outdoor environments using a variety of sensors: a laser scanner, sonar rangers, and omni-directional video. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) In this paper, we suggest a new method of human augmented mapping for indoor environments using only a stereo camera. Through user's help, a robot with a stereo camera can investigate the environment without failure and even more efficiently. Moreover, the user can share the information about the environment with the robot and add semantic information to the environmental map. We employ PCA features for visual landmarks and a hybrid map for map representation. Particularly, we define two types of nodes, U R-nodes and divide the map building into three processes, User's Guidance, Robot's Map Revision, and Robot's Map Completion. We implemented a human augmented mapping system with a stereo camera and demonstrated it in rectangular-shaped corridors. From the comparison with a manually-built map, we showed the feasibility of the environmental map generated by our proposed method.