A system and method for estimating a pose of an imaging device for one or more images is provided.

The disclosed system may include a housing dimensioned to secure various components including at least one physical processor and various sensors. The system may also include a camera mounted to the housing, as well as physical memory with computer-executable instructions that, when executed by the physical processor, cause the physical processor to: acquire images of a surrounding environment using the camera mounted to the housing, identify features of the surrounding environment from the acquired images, generate a map using the features identified from the acquired images, access sensor data generated by the sensors, and determine a current pose of the system in the surrounding environment based on the features in the generated map and the accessed sensor data. Various other methods, apparatuses, and computer-readable media are also disclosed.

Various techniques are provided performing temperature inspections of assets, such as temperature-sensitive industrial equipment. In one example, a method includes receiving, at a portable device, inspection instructions associated with an asset at a location in an environment. The method also includes displaying, at the portable device, the asset in an augmented reality format to guide a user of the portable device to the location. The method also includes capturing, by a thermal imager associated with the portable device, a thermal image of the asset when the thermal imager is aligned with the asset. The method also includes extracting, from the thermal image, at least one temperature measurement associated with the asset. Additional methods and systems are also provided.

Embodiments of localization and mapping using depth modeling are described herein. In one example, frames of image data captured by sensor(s) from various poses within an environment are received over an interface. Keypoints are detected in the current frame, and matching keypoints are found in preceding frames. The pose of the current frame is determined based at least partially on depth models associated with the matching keypoints.

The disclosure provides a feature point position detection method and an electronic device. The method includes: obtaining a plurality of first relative positions of a plurality of feature points on a specific object relative to a first image capturing element; obtaining a plurality of second relative positions of the plurality of feature points on the specific object relative to a second image capturing element; and in response to determining that the first image capturing element is unreliable, estimating a current three-dimensional position of each feature point based on a historical three-dimensional position and the plurality of second relative positions of each feature point.

An own-position estimating device for estimating an own-position of a moving body by matching a feature extracted from an acquired image with a database in which position information and the feature are associated with each other in advance, includes an evaluation result acquiring unit acquiring an evaluation result obtained by evaluating matching eligibility of the feature in the database, and a processing unit processing the database on the basis of the evaluation result acquired by the evaluation result acquiring unit.

A method for calibrating an active FOV of a single camera, wherein from the calibration of the cameras active FOV, a coordinate matrix is obtained which remotely produces a virtual interpolation measurement network at any point within an image (a frame) extracted from a video stream (recorded by the single camera), while eliminating the need to be physically located at the actual location where the video stream has been recorded. According to an embodiment of the invention, the basis of the active FOV of a camera is the ability to obtain (measure) coordinates of the measurement points marked on a calibration board.

Disclosed are a face image and iris image acquisition method and device, a computer-readable readable storage medium and an apparatus. The method includes rotating the first tripod head to force the face lens and the iris lens to be in acquisition positions; capturing a first face image and a first iris image simultaneously by the face lens and the iris lens; and locating the iris in the first iris image, and if no iris is located, determining whether a condition of light-avoiding rotation is satisfied, and if the condition is satisfied, rotating the second tripod head to adjust an angle or a position of the supplementary light source to enable a light spot region to avoid an iris region.

An image processing apparatus includes a reception unit that receives an image signal acquired by an image sensor, an acceptance unit that accepts operation input made by a user for driving a device carrying the image sensor, a calculation unit that calculates a movement amount of the image sensor in an entire angle of view according to the image signal and the operation input, and a correction processing unit that performs a correction process for an image constructed from the image signal, according to the movement amount.

A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.