A cross reality system enables any of multiple devices to efficiently and accurately access previously persisted maps, even maps of very large environments, and render virtual content specified in relation to those maps. The cross reality system may quickly process a batch of images acquired with a portable device to determine whether there is sufficient consistency across the batch in the computed localization. Processing on at least one image from the batch may determine a rough localization of the device to the map. This rough localization result may be used in a refined localization process for the image for which it was generated. The rough localization result may also be selectively propagated to a refined localization process for other images in the batch, enabling rough localization processing to be skipped for the other images.

A method for dynamic loop closure in a mobile automation apparatus includes: obtaining mapping trajectory data defining a plurality of trajectory segments traversing a facility to be mapped; controlling a locomotive mechanism of the apparatus to traverse a current segment; generating a sequence of keyframes for the current segment using sensor data captured via a navigational sensor of the apparatus; and, for each keyframe: determining an estimated apparatus pose based on the sensor data and a preceding estimated pose corresponding to a preceding keyframe; and, determining a noise metric defining a level of uncertainty associated with the estimated pose relative to the preceding estimated pose; determining, for a selected keyframe, an accumulated noise metric based on the noise metrics for the selected keyframe and each previous keyframe; and when the accumulated noise metric exceeds a threshold, updating the mapping trajectory data to insert a repetition of one of the segments.

A system configured to assist a user in scanning a physical environment in order to generate a three-dimensional scan or model. In some cases, the system may include an interface to assist the user in capturing data usable to determine a scale or depth of the physical environment and to perform a scan in a manner that minimizes gaps.

A camera array, an imaging device and/or a method for capturing image that employ a plurality of imagers fabricated on a substrate is provided. Each imager includes a plurality of pixels. The plurality of imagers include a first imager having a first imaging characteristics and a second imager having a second imaging characteristics. The images generated by the plurality of imagers are processed to obtain an enhanced image compared to images captured by the imagers. Each imager may be associated with an optical element fabricated using a wafer level optics (WLO) technology.

A method for predicting a direction of movement of a target object, a method for training a neural network, a smart vehicle control method, a device, an electronic apparatus, a computer readable storage medium, and a computer program. The method for predicting a direction of movement of a target object comprises: acquiring an apparent orientation of a target object in an image captured by a camera device, and acquiring a relative position relationship of the target object in the image and the camera device in three-dimensional space (S100); and determining, according to the apparent orientation of the target object and the relative position relationship, a direction of movement of the target object relative to a traveling direction of the camera device (S110).

Provided is an information processing apparatus, including a position estimating unit configured to estimate a position of a second imaging apparatus on the basis of a first captured image captured by a first imaging apparatus whose position is specified and a second captured image captured at a time corresponding to the first captured image by the second imaging apparatus serving as a position estimation target.

A method comprising: obtaining a three-dimensional (3D) point cloud about an object; obtaining binary feature descriptors for feature points in a 2D image about the object; assigning a plurality of index values for each feature point as multiple bits of the corresponding binary feature descriptor; storing the binary feature descriptor in a table entry of a plurality of hash key tables of a database image; obtaining query binary feature descriptors for feature points in a query image; matching the query binary feature descriptors to the binary feature descriptors of the database image; reselecting one bit of the hash key of the matched database image; and re-indexing the feature points in the table entries of the hash key table of the database image.

According to one embodiment, a position and attitude estimation device includes a processor. The processor is configured to acquire time-series images continuously captured by a capture device installed on a mobile object, estimate first position and attitude of the mobile object based on the acquired time-series images, estimate a distance to a subject included in the acquired time-series images and correct the estimated first position and attitude to a second position and attitude based on an actual scale, based on the estimated distance.

To enable shared user experiences using augmented reality systems, shared reference points must be provided to have consistent placement (position and orientation) of virtual objects. Furthermore, the position and orientation (pose) of the users must be determinable with respect to the same shared reference points. However, without highly sensitive and expensive global positioning system (GPS) devices, pose information can be difficult to determine to a reasonable level of accuracy. Therefore, what is provided is an alternative approach to determining pose information for augmented reality systems, which can be used to perform location based content acquisition and sharing. Further, what is provided is an alternative approach to determining pose information for augmented reality systems that uses information from already existing GPS devices.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.