The present application discloses a pose estimation method and device, related equipment and a storage medium. In the pose estimation method, a visual observation model constraint is constructed according to images, a motion model constraint is constructed according to inertial data, and a pose of a camera, an inertial measurement unit or an object that corresponds to the time of each image is calculated according to the visual observation model constraint and the motion model constraint. The pose estimation method adopts a fusion model of the visual observation model constraint and the motion model constraint to improve the accuracy, stability and robustness of pose estimation in a complex scene.

An object is to provide a pavement condition rating system which utilizes a road surface image based on perspective and which is extracted from a moving image obtained by traveling while photographing a road surface of a road, makes it possible to use the whole of an image area of a road surface included in a road surface image for detection of damage, and is less susceptible to damage erroneous detection. By using a plurality of images obtained by combining image of damage included in a road surface still image based on perspective and definitions of the damage as damage learning data, an object detection model is built. By using a plurality of data wherein the number of rectangles in an analysis result image displaying the rectangles surrounding the damage output as an analysis result and a pavement condition rating result obtained by visual inspection of the analysis result image are combined as condition rate learning data, a rating estimation model is built, and the pavement condition is rated using the rating estimation model.

Techniques and systems are provided for determining one or more sizes of one or more objects. For example, a bounding region identifying a first object detected in an image can be obtained. A map including map points can also be obtained. The map points correspond to one or more reference locations in a three-dimensional space. The bounding region identifying the first object can be associated with at least one map point of the map points included in the map. Using the bounding region and the at least one map point, an estimated three-dimensional position and an estimated size of the first object detected in the image can be determined. In some examples, other information can be used to estimate the estimated three-dimensional position and an estimated size of the first object, such as radar information and/or other information.

Apparatuses, systems, and techniques to estimate or predict depth information for image data. In at least one embodiment, depth information is predicted based at least in part on color information and geometry information associated with an image.

The present disclosure relates to an XR device and a method for controlling the same, and more particularly, is applicable to a 5G communication technology field, a robot technology field, an autonomous technology field and an artificial intelligence (AI) technology field. The method for controlling an XR device of a vehicle includes acquiring a camera view by capturing an image in front of the vehicle; acquiring position information of the vehicle by detecting a position of the vehicle, acquiring movement information of the vehicle by detecting movement of the vehicle, and providing navigation of an augmented reality (AR) mode displaying at least one virtual object for guiding a path by overlapping the at least one virtual object on the camera view based on at least the position information of the vehicle or the movement information of the vehicle.

The present application relates to a method and an apparatus for detecting lane lines, an electronic device and a non-transitory storage medium. The method comprises: acquiring an image to be detected; determining at least one initial point in the image; extracting a position characteristic of at least one initial point; processing the position characteristic of the at least one initial point by using a first network model to obtain trend information of a corresponding lane line; and generating a target lane line containing the at least one initial point according to the trend information. In the present application, a network model is used to process a position characteristic of an initial point to obtain trend information of the lane line, and a complete lane line of the road image is quickly generated according to the trend information.

Disclosed herein are methods and systems for detecting dynamic objects using road painted patterns perceptible in infrared spectral range, comprising receiving images captured in one or more infrared spectral ranges depicting a road segment painted with background patterns which are highly imperceptible in visible light spectrum while highly visible in one or more infrared spectral ranges, analyzing the images to detecting one or more dynamic objects located in front of the background patterns. The light reflected by the one or more dynamic objects in the one or more infrared spectral ranges deviating from the light reflected by the one or more background pattern and computing a location of the one or more identified objects. Further disclosed are methods and systems for calibration of systems and/or sensors based on reference markings which are highly imperceptible in visible light spectrum while highly visible in the infrared spectral range(s).

Systems and methods for segmenting scan data are disclosed. The methods include receiving scan data representing a plurality of points in an environment associated with a ground surface and one or more objects, and creating a graph from the scan data. The graph includes a plurality of vertices corresponding to the plurality of points. The method further includes assigning a unary potential to each of the plurality of vertices that is a cost of assigning that vertex to a ground label or a non-ground label, and assigning a pairwise potential to each pair of neighboring vertices in the graph that is the cost of assigning different labels to neighboring vertices. The methods include using the unary potentials and the pairwise potentials to identify labels for each of the plurality of points, and segmenting the scan data to identify points associated with the ground based on the identified labels.

A method includes acquiring at least one image with at least one camera associated with at least one mobile robot; and extracting a plurality of straight lines from the at least one image; creating at least one dataset comprising data related to the plurality of straight lines extracted from the at least one image; forming a plurality of hypotheses for a walkway boundary based on the at least one dataset and determining at least one hypothesis with the highest likelihood of representing a walkway boundary; and using the at least one hypothesis to adjust a direction and/or speed of motion of the at least one mobile robot.

According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.