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:
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 the use of user equipment (UE) for the generation of a 3D exterior envelope of a structure based on captured images and a measurement set associated with each captured image. In some embodiments, a sequence of exterior images of a structure is captured and a corresponding measurement set comprising Inertial Measurement Unit (IMU) measurements, wireless measurements (including Global Navigation Satellite (GNSS) measurements) and/or other non-wireless sensor measurements may be obtained concurrently. A closed-loop trajectory of the UE in global coordinates may be determined and a 3D structural envelope of the structure may be obtained based on the closed loop trajectory and feature points in a subset of images selected from the sequence of exterior images of the structure.
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 mobile device tracks a relative pose between a camera and a target using Vision aided Inertial Navigation System (VINS), that includes a contribution from inertial sensor measurements and a contribution from vision based measurements. When the mobile device detects movement of the target, the contribution from the inertial sensor measurements to track the relative pose between the camera and the target is reduced or eliminated. Movement of the target may be detected by comparing vision only measurements from captured images and inertia based measurements to determine if a discrepancy exists indicating that the target has moved. Additionally or alternatively, movement of the target may be detected using projections of feature vectors extracted from captured images.
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:
Systems, apparatus and methods in a mobile device to enable and disable a depth sensor for tracking pose of the mobile device are presented. A mobile device relaying on a camera without a depth sensor may provide inadequate pose estimates, for example, in low light situations. A mobile device with a depth sensor uses substantial power when the depth sensor is enabled. Embodiments described herein enable a depth sensor only when images are expected to be inadequate, for example, accelerating or moving too fast, when inertial sensor measurements are too noisy, light levels are too low or high, an image is too blurry, or a rate of images is too slow. By only using a depth sensor when images are expected to be inadequate, battery power in the mobile device may be conserved and pose estimations may still be maintained.
Abstract:
Systems, apparatus and methods for triggering a depth sensor and/or limiting bandwidth and/or maintaining privacy are presented. By limiting use of a depth sensor to times when an optical image alone is insufficient, mobile device power is saved. Furthermore, by reducing a size of an optical image to only the portion of the image needed to detect an object, bandwidth is saved and privacy is maintained by not communicating unneeded or undesired information.