Abstract:
An apparatus and method for generating parameters for an application, such as an augmented reality application (AR app), using camera pose and gyroscope rotation is disclosed. The parameters are estimated based on pose from images and rotation from a gyroscope (e.g., using least-squares estimation with QR factorization or a Kalman filter). The parameters indicate rotation, scale and/or non-orthogonality parameters and optionally gyroscope bias errors. In addition, the scale and non-orthogonality parameters may be used for conditioning raw gyroscope measurements to compensate for scale and non-orthogonality.
Abstract:
A mobile device compensates for lack of a time stamp when an image frame is captured by estimating the frame time stamp latency. The mobile device captures images frames and time stamps each frame after the frame time stamp latency. A vision based rotation is determined from a pair of frames. A plurality of inertia based rotations is measured using time stamped signals from an inertial sensor in the mobile device based on different possible delays between time stamping each frame and time stamps on the signals from the inertial sensors. The determined rotations may be about the camera's optical axis. The vision based rotation is compared to the plurality of inertia based rotations to determine an estimated frame time stamp latency, which is used to correct the frame time stamp latency when time stamping subsequently captured frames. A median latency determined using different frame pairs may be used.
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:
Method, apparatus, and computer program product for merging multiple maps for computer vision based tracking are disclosed. In one embodiment, a method of 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 merging the multiple keyframes to generate a global map of the scene.
Abstract:
An apparatus and method for generating parameters for an application, such as an augmented reality application (AR app), using camera pose and gyroscope rotation is disclosed. The parameters are estimated based on pose from images and rotation from a gyroscope (e.g., using least-squares estimation with QR factorization or a Kalman filter). The parameters indicate rotation, scale and/or non-orthogonality parameters and optionally gyroscope bias errors. In addition, the scale and non-orthogonality parameters may be used for conditioning raw gyroscope measurements to compensate for scale and non-orthogonality.
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:
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.