Abstract:
Systems and methods for performing localization and mapping with a mobile device are disclosed. In one embodiment, a method for performing localization and mapping with a mobile device includes identifying geometric constraints associated with a current area at which the mobile device is located, obtaining at least one image of the current area captured by at least a first camera of the mobile device, obtaining data associated with the current area via at least one of a second camera of the mobile device or a sensor of the mobile device, and performing localization and mapping for the current area by applying the geometric constraints and the data associated with the current area to the at least one image.
Abstract:
Embodiments disclosed pertain to systems, method s and apparatus for the initialization of Computer Vision (CV) applications on user devices (UDs) comprising a camera and a display. In some embodiments, an optimal camera trajectory for initialization of a Computer Vision (CV) application may be determined based on an initial camera pose and an estimated pivot distance. For example, the initial camera pose may be estimated based on a first image captured by the camera. Further, the display may be updated in real-time with an indication of a desired movement direction for the camera. In some embodiments, the indication of desired movement direction may be based, in part, on a current camera pose and the optimal trajectory, where the current camera pose may be estimated based on a current image captured by the camera.
Abstract:
Embodiments of the present invention are directed toward providing intelligent sampling strategies that make efficient use of an always-on camera. To do so, embodiments can utilize sensor information to determine contextual information regarding the mobile device and/or a user of the mobile device. A sampling rate of the always-on camera can then be modulated based on the contextual information.
Abstract:
Methods and apparatus relating to enabling augmented reality applications using eye gaze tracking are disclosed. An exemplary method according to the disclosure includes displaying an image to a user of a scene viewable by the user, receiving information indicative of an eye gaze of the user, determining an area of interest within the image based on the eye gaze information, determining an image segment based on the area of interest, initiating an object recognition process on the image segment, and displaying results of the object recognition process.
Abstract:
An accelerometer in a mobile device is calibrated by taking multiple measurements of acceleration vectors when the mobile device is held stationary at different orientations with respect to a plane normal. A circle is calculated that fits respective tips of measured acceleration vectors in the accelerometer coordinate system. The radius of the circle and the lengths of the measured acceleration vectors are used to calculate a rotation angle for aligning the accelerometer coordinate system with the mobile device surface. A gyroscope in the mobile device is calibrated by taking multiple measurements of a rotation axis when the mobile device is rotated at different rates with respect to the rotation axis. A line is calculated that fits the measurements. The angle between the line and an axis of the gyroscope coordinate system is used to align the gyroscope coordinate system with the mobile device surface.
Abstract:
Method, apparatus, and computer program product for merging multiple maps for computer vision based tracking comprises receiving a plurality of maps of a scene in a venue from at least one mobile device, identifying multiple keyframes of the plurality of maps of the scene, and eliminating redundant keyframes to generate a global map of the scene.
Abstract:
Reference free tracking of position by a mobile platform is performed using images of a planar surface. Tracking is performed optical flow techniques, such as pyramidal Lucas-Kanade optical flow with multiple levels of resolution, where displacement is determined with pixel accuracy at lower resolutions and at sub-pixel accuracy at full resolution, which improves computation time for real time performance. Periodic drift correction is performed by matching features between a current frame and a keyframe. The keyframe may be replaced with the drift corrected current image.
Abstract:
A mobile device determines a vision based pose using images captured by a camera and determines a sensor based pose using data from inertial sensors, such as accelerometers and gyroscopes. The vision based pose and sensor based pose are used separately in a visualization application, which displays separate graphics for the different poses. For example, the visualization application may be used to calibrate the inertial sensors, where the visualization application displays a graphic based on the vision based pose and a graphic based on the sensor based pose and prompts a user to move the mobile device in a specific direction with the displayed graphics to accelerate convergence of the calibration of the inertial sensors. Alternatively, the visualization application may be a motion based game or a photography application that displays separate graphics using the vision based pose and the sensor based pose.
Abstract:
A system and method is described herein for solving for surface normals of objects in the scene observed in a video stream. The system and method may include sampling the video stream to generate a set of keyframes; generating hypothesis surface normals for a set of mappoints in each of the keyframes; warping patches of corresponding mappoints in a first keyframe to the viewpoint of a second keyframe with a warping matrix computed from each of the hypothesis surface normals; scoring warping errors between each hypothesis surface normal in the two keyframes; and discarding hypothesis surface normals with high warping errors between the first and second keyframes.
Abstract:
Systems, apparatus and methods for estimating gravity and/or scale in a mobile device are presented. A difference between an image-based pose and an inertia-based pose is using to update the estimations of gravity and/or scale. The image-based pose is computed from two poses and is scaled with the estimation of scale prior to the difference. The inertia-based pose is computed from accelerometer measurements, which are adjusted by the estimation for gravity.