Provided is a robot, including: a plurality of sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with an image sensor, images of a workspace as the robot moves within the workspace; identifying, with the processor, at least one characteristic of at least one object captured in the images of the workspace; determining, with the processor, an object type of the at least one object based on characteristics of different types of objects stored in an object dictionary; and instructing, with the processor, the robot to execute at least one action based on the object type of the at least one object.

The present application discloses a method and an apparatus of obstacle three-dimensional position acquisition for a roadside computing device, and relates to the fields of intelligent transportation, cooperative vehicle infrastructure, and autonomous driving. The method may include: acquiring pixel coordinates of an obstacle in a to-be-processed image; determining coordinates of a bottom surface center point of the obstacle according to the pixel coordinates; acquiring a homography relationship between a ground surface corresponding to the to-be-processed image and a ground surface corresponding to a template image; transforming the coordinates of the bottom surface center point of the obstacle into coordinates on the template image according to the homography relationship; and determining three-dimensional coordinates of the bottom surface center point of the obstacle according to the coordinates obtained by transformation and a ground equation corresponding to the template image. By use of the solution of the present application, implementation costs can be saved, and the accuracy is better.

A method for visualizing data generated by a robotic device is presented. The method includes displaying an intended path of the robotic device in an environment. The method also includes displaying a first area in the environment identified as drivable for the robotic device. The method further includes receiving an input to identify a second area in the environment as drivable and transmitting the second area to the robotic device.

An imagery processing system determines pixel color values for pixels of captured imagery from volumetric data, providing alternative pixel color values. A main imagery capture device, such as a camera, captures main imagery such as still images and/or video sequences, of a live action scene. Alternative devices capture imagery of the live action scene, in some spectra and form, and capture information related to pixel color values for multiple depths of a scene, which can be processed to provide reconstruction.

Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.

Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

A method of detecting damages in a freight container, comprising capturing a first image of a part of the freight container at an angle deviating from a perpendicular direction in respect of the part of the container, capturing a second image of the same part of the container at an angle substantially perpendicular in respect of the same part of the container, analysing the first and second images for detecting a damage in the part of the container, and in response to detecting the damage in the part of the container providing a damage information image regarding to the part of the container, the damage information image being an image based on the second image regarding to the respective part of the container and including damages detected in at least one of the respective first image or this second image.

Also, a system for detecting damages in a freight container.

A system and method for machine learning model validation. A method includes: determining a first score distribution for a first run of a machine learning model and a second score distribution for a second run of the machine learning model, wherein the first run includes applying the machine learning model to a first test dataset, wherein the second run includes applying the machine learning model to a second test dataset, wherein the second test dataset is collected after the first test dataset; comparing the first score distribution to the second score distribution; determining, based on the comparison, whether the machine learning model is validated; continuing use of the machine learning model when it is determined that the machine learning model is validated; and performing at least one rehabilitative action with respect to the machine learning model when it is determined that the machine learning model is not validated.

A method of recognizing target objects in images obtains a detection image of a target object. A template image is generated according to the target object. The detection image is compared with the template image to obtain a comparison result. Candidate regions of the target object are determined in the detection image according to the comparison result. At least one target region of the target object is obtained from the candidate regions. The method detects target objects in images very rapidly.

A finger-worn wearable ring device may include a ring-shaped housing, a printed circuit board, and a sensor module that includes one or more light-emitting components and one or more light-receiving components. The wearable ring device may further include a communication module configured to wirelessly communicate with an application executable on a user device.