We present 3DTouch, a novel 3D wearable input device worn on the fingertip for 3D manipulation tasks. 3DTouch is designed to fill the missing gap of a 3D input device that is self-contained, mobile, and universally working across various 3D platforms. This paper presents a low-cost solution to designing and implementing such a device. Our approach relies on relative positioning technique using an optical laser sensor and a 9-DOF inertial measurement unit. 3DTouch is self-contained, and designed to universally work on various 3D platforms. The device employs touch input for the benefits of passive haptic feedback, and movement stability. On the other hand, with touch interaction, 3DTouch is conceptually less fatiguing to use over many hours than 3D spatial input devices. We propose a set of 3D interaction techniques including selection, translation, and rotation using 3DTouch. An evaluation also demonstrates the device's tracking accuracy of 1.10 mm and 2.33 degrees for subtle touch interaction in 3D space. Modular solutions like 3DTouch opens up a whole new design space for interaction techniques to further develop on.
Inertial tracking systems, on the other hand, can be self-contained and require no external reference. They use technologies such as accelerometers, gyroscopes, and compasses to sense their change in orientation @cite_2 . While devices equipped with such sensors (e.g., Wiimote, air mice, and smartphones) are capable of serving as a 3D pointing device, they have only been used to translate objects on a fixed 2D plane (e.g., the TV screen). 3DTouch, with 5 degrees of freedom, does not only serve as a 3D pointing device, but also enables users to rotate and translate objects in 3D space.
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Augmented Reality applications require the tracking of moving objects in real-time. Tracking is defined as the measurement of object position and orientation in a scene coordinate system. We present a new combination of silicon micromachined accelerometers and gyroscopes which have been assembled into a six degree of freedom (6 DoF) inertial tracking system. This inertial tracker is used in combination with a vision-based tracking system which will enable us to build affordable, light-weight, fully mobile tracking systems for Augmented Reality applications in the future. We report results from a formal user study of interactive 3D rotation using the mouse-driven Virtual Sphere and Arcball techniques, as well as multidimensional input techniques based on magnetic orientation sensors. MultidimensionaI input is often assumed to allow users to work quickly, but at the cost of precision, due to the instability of the hand moving in the open air. We show that, at least for the orientation matching task used in this experiment, users can take advantage of the integrated degrees of freedom provided by multidimensional input without necessarily sacrificing precision: using multidimensional input, users completed the experimental task up to 36 faster without any statistically detectable loss of accuracy. We also report detailed observations of common usability problems when first encountering the techniques. Our observations suggest some design issues for 3D input devices. For example, the physical form-factors of the 3D input device significantly influenced user acceptance of otherwise identical input sensors. The device should afford some tactile cues, so the user can feel its orientation without looking at it. In the absence of such cues, some test users were unsure of how to use the device. While global navigation satellite systems (GNSS) are the state of the art for localization, in general they are unable to operate inside buildings, and there is currently no well-established solution for indoor localization. In this paper we propose a 3D mobile robot pose (2D position and 1D orientation) estimation system for indoor applications. The system is based on the cooperative sensor fusion of radar, ultrasonic and odometry data using an extended Kalman filter (EKF). A prerequisite for the EKF is an occupancy grid map of the scenario as well as the pose of the reference radar node inside the map. Our system can handle even the kidnapped-robot case as the radar provides absolute localization. We conducted a series of measurements in an office building corridor. We determined the typical position root-mean square error (RMSE) to be less than 15 cm. This paper proposes an ego-motion tracking method that utilizes visual-inertial sensors for wearable blind navigation. The unique challenge of wearable motion tracking is to cope with arbitrary body motion and complex environmental dynamics. We introduce a visual sanity check to select accurate visual estimations by comparing visually estimated rotation with measured rotation by a gyroscope. The movement trajectory is recovered through adaptive fusion of visual estimations and inertial measurements, where the visual estimation outputs motion transformation between consecutive image captures, and inertial sensors measure translational acceleration and angular velocities. The frame rates of visual and inertial sensors are different, and vary with respect to time owning to visual sanity checks. We hence employ a multirate extended Kalman filter (EKF) to fuse visual and inertial estimations. The proposed method was tested in different indoor environments, and the results show its effectiveness and accuracy in ego-motion tracking.