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In this paper, autonomous switching between two basic attention selection mechanisms, i.e., top-down and bottom-up, is proposed. This approach fills a gap in object search using conventional top-down biased bottom-up attention selection, which fails, if a group of objects is searched whose appearances cannot be uniquely described by low-level features used in bottom-up computational models. Three...
In this paper an autonomous switching between two basic attention selection mechanisms, top-down and bottom-up, is proposed, substituting manual switching. This approach fills the gab in object search using conventional top-down biased bottom-up attention selection: the latter one fails, if a group of objects is searched whose appearances can not be uniquely described by low-level features used in...
In the project Autonomous City Explorer, an interactive robot is designed to find its way to a given destination in unknown urban environments by interacting with pedestrians. Considering applications in a human dominated environment, the robot can be sent to a destination by tracking a landmark selected by users and described by 2D image features. To achieve a natural landmark selection from the...
In the autonomous city explorer (ACE) project a mobile robot is developed, which is capable of finding its way to a given destination in an unknown urban environment. An exemplary mission is to find the way from our institute to the Marienplatz, a public place in the center of Munich, without any prior knowledge or GPS information. Inspired by the behavior of humans in unknown environments, ACE must...
In this paper a novel implementation of the saliency map model on a multi-GPU platform using CUDA technology is presented. The saliency map model is a well-known computational model for bottom-up attention selection and serves as a basis of many attention control strategies of cognitive vision systems. A real-time implementation is the prerequisite of an application of bottom-up attention on mobile...
This video presents the Autonomous City Explorer (ACE) project. Its goal was to create a robot capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. The robot had to find its way solely by interacting with pedestrians and building a topological representation of its surroundings. This video outlines the necessary ingredients for successful low-level navigation...
Goal-directed guidance of gaze control based on coordinated task and stimulus parameters is essential for steering a mobile cognitive system efficiently and autonomously through the real world. This paper focuses on coordination mechanisms of top-down and bottom-up attentional allocation, with particular consideration of the current local environment. The top-down attention selection in the task-space...
A multi-camera view direction planning strategy for mobile robots is discussed. Two concurrent tasks are considered: self-localization and object tracking. The approach is to assign the different tasks to different cameras, such that for each task an individual optimal view direction is selected based on the information gain maximization. Thereby, the individual task performance is significantly improved...
As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. Not only do they have to operate efficiently and safely in natural populated environments, but also be able to achieve higher levels of cooperation and interaction with humans. The autonomous city explorer (ACE) project envisions...
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