Augmented reality devices and methods for computing a homography based on two images. One method may include receiving a first image based on a first camera pose and a second image based on a second camera pose, generating a first point cloud based on the first image and a second point cloud based on the second image, providing the first point cloud and the second point cloud to a neural network, and generating, by the neural network, the homography based on the first point cloud and the second point cloud. The neural network may be trained by generating a plurality of points, determining a 3D trajectory, sampling the 3D trajectory to obtain camera poses viewing the points, projecting the points onto 2D planes, comparing a generated homography using the projected points to the ground-truth homography and modifying the neural network based on the comparison.

A method and a system described herein provide sensor-level based data stream processing. In particular, concepts of enabling low level sensor fusion by lightweight semantic segmentation on sensors generating point cloud as generated from LIDAR, radar, cameras and Time-of-Flight sensors are described. According to the present disclosure a computer-implemented method for sensor-level based data stream processing comprises receiving a first data stream from a LIDAR sensor, removing a ground from the point cloud, performing clustering on the point cloud, and feature processing on the point cloud. The point cloud represents a set of data points in space.

A system and method are provided for autonomously navigating a vehicle. The method captures a sequence of image pairs using a stereo camera. A navigation application stores a vehicle pose (history of vehicle position). The application detects a plurality of matching feature points in a first matching image pair, and determines a plurality of corresponding object points in three-dimensional (3D) space from the first image pair. A plurality of feature points are tracked from the first image pair to a second image pair, and the plurality of corresponding object points in 3D space are determined from the second image pair. From this, a vehicle pose transformation is calculated using the object points from the first and second image pairs. The rotation angle and translation are determined from the vehicle pose transformation. If the rotation angle or translation exceed a minimum threshold, the stored vehicle pose is updated.

According to an embodiment, an estimation apparatus includes a memory and a processor. The processor acquires a first measurement point groups obtained by measuring a periphery of a first moving object. The processor estimates a position and posture of the first moving object. The processor classifies first measurement points serving as candidates of measurement points on a second moving object in the newest first measurement point group as candidate points. The processor acquires second moving object information from the second moving object. The processor calculates an evaluation value using a first likelihood defined according to a position relationship between an orientation of a region specified from the second moving object information and the candidate points. The processor estimates a position and posture of the second moving object based on the evaluation value.

A method, including receiving a two-dimensional (2D) image of a three-dimensional (3D) object recorded at a first angle of rotation of the object, and identifying, in the 2D image, a set of image descriptors, each of the image descriptors including an image keypoint and one or more image features. The set of image descriptors are compared against sets of template descriptors for respective previously captured 2D images, each of the template descriptors comprising a template keypoint and one or more template features. Using a threshold, a given set of template descriptors matching the set of image descriptors are identified, the given set of template descriptors corresponding to a given previously captured 2D image of the 3D object recorded at a second angle of rotation of the object. Any of the image descriptors not in the given set of the template descriptors are added to the given set of template descriptors.

An approach is provided for predicting a pose error for a sensor system based on a trained machine learning model. The approach, for example, involves receiving images depicting a survey point with a known physical location. The approach also involves determining meta-data associated with the sensor system used to capture the images. The approach further involves generating a ray from the capture location through a pixel location of the survey point on an image plane of each image. The approach further involves calculating an error between the ray generated for the image and the known physical location. The approach further involves training a machine learning model to predict a pose error from image data captured using the sensor system based on the error in combination with features extracted from the image and the meta-data for the image. The approach further involves providing the trained machined learning as an output.

An electronic device and method are disclosed. The electronic device includes a cover glass, a display panel, a fingerprint sensor, and a processor. The processor implements the method, including: obtaining, through a fingerprint sensor, a fingerprint image from an external object contacting a surface of the electronic device, detecting, using a processor, a feature point of the fingerprint image, comparing the detected feature point of the fingerprint image with a feature point of a pre-stored reference image, detecting a variation in a distance between the surface of the electronic device and the fingerprint sensor based on a result of the comparison, and recognizing the fingerprint based on the detected variation in the distance.

A method for profiling a location of an image capture device in a region of interest. The method comprises acquiring an image captured by an image capture device. The method comprises acquiring a reported position and pose of the image capture device. The method comprises processing the captured image to detect an object in the captured image. The method comprises identifying a set of interest points characterizing the captured object. The method comprises generating a relative position and pose of the interest points based on dimensional information associated with the captured object. The method comprises computing an estimated position and pose of the image capture device to the object using the reported position and pose of the image capture device and the relative position of the interest points. The method comprises computing the estimated position and pose of the mobile imaging device based on the estimated distance. The method comprises updating the reported position and pose of the image capture device to the estimated position.

A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person. The method includes extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of a posture of the person in the image frame; deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating a progression of a movement of the person; detecting a start loop condition by evaluating the time progression of the at least one characteristic parameter, said start loop condition indicating a transition from a start posture of the person to the movement of the person when performing the physical exercise, wherein a loop of exercising encompasses one single repetition of the physical exercise; detecting an end loop condition by evaluating the time progression of at least one of the characteristic parameters, said end loop condition indicating a transition from the movement of the person when performing the physical exercise to an intermediate posture, wherein, as a result, the start of the loop and the end of the loop are determined; and deriving the time period for a single loop of the physical exercise based on the start of the loop and the end of the loop and evaluating the time period.

A system and method for pipeline data acquisition may include a software program that can autonomously review new and legacy videos collected by camera-equipped robotic systems from inside the pipelines, and automatically detect and categorize different features. Three-dimensional (3-D) point clouds may also be generated using software algorithms that stitch together like features in different video frames.