Commercial 3D scene acquisition systems such as the Microsoft Kinect sensor can reduce the cost barrier of realizing mid-air interaction. However, since it can only sense hand position but not hand orientation robustly, current mid-air interaction methods for 3D virtual object manipulation often require contextual and mode switching to perform translation, rotation, and scaling, thus preventing natural continuous gestural interactions. A novel handle bar metaphor is proposed as an effective visual control metaphor between the user's hand gestures and the corresponding virtual object manipulation operations. It mimics a familiar situation of handling objects that are skewered with a bimanual handle bar. The use of relative 3D motion of the two hands to design the mid-air interaction allows us to provide precise controllability despite the Kinect sensor's low image resolution. A comprehensive repertoire of 3D manipulation operations is proposed to manipulate single objects, perform fast constrained rotation, and pack/align multiple objects along a line. Three user studies were devised to demonstrate the efficacy and intuitiveness of the proposed interaction techniques on different virtual manipulation scenarios.

While very large collaborative surfaces are already being widely employed to facilitate concurrent interactions with multiple users, they involve no personalization in the touch interactions. Augmenting them to identify the touch interactions with multiple smart-phones can enable interesting co-located communal applications with context-based personalized interactions and information exchange amongst users' portable devices and the shared wall display. This paper proposes a novel matching technique, called tilt correlation, which employs the built-in tilt sensor to identify smart-phones that make concurrent two-point contacts on a common multi-touch wall display. Experimental investigations suggest that the resultant error rate is relatively low; in addition, we also propose a quantitative measure, called the Bourne Identity Index to allow application designers to determine the reliability of each device identification.

This paper presents an implementation of place cells for a robot navigation using the K-iterations fast learning artificial neural networks (KFLANN) clustering algorithm. The KFLANN possesses several desirable properties suitable for place cell robot navigation tasks. The technique proposed is able to autonomously adjust the resolution of cells according to the complexity of the environment. This is achieved through two parameters known as the tolerance and the vigilance of the network. In addition, a navigation system consisting of a topological map building and a place cell path planning strategy is presented. A physical implementation of the system was developed on an autonomous platform and actual results were obtained. The experimental results obtained indicate that the system was able to navigate successfully through the experimental space and also tolerate unexpected discrepancies arising from motor and sensor errors present in a real environment. Furthermore, despite abrupt changes in an environment due to the deliberate introduction of obstacles, the system was still able to cope without changes to the program. The experiment was also extended to include a kidnapped robot scenario and the results were favorable, indicating a positive use of allothetic cue recognition capabilities.