A map database creation method is provided. The method includes: obtaining a factor set including factors; dividing a map database into levels based on the factors, and taking each interval of the last level as one sub-database; creating an initial map based on a factor value of each factor corresponding to each sub-database, and creating the sub-database as an initial map database by storing the corresponding initial map in the sub-database; finding the initial map matching a current lighting condition from the initial map database based on the current lighting condition, and taking the found initial map as a positioning map; and performing a visual positioning based on the positioning map, creating an expanded map corresponding to the current lighting condition based on the visual positioning, and creating the sub-database corresponding to the current lighting condition as an expanded map database by storing the corresponding expanded map in the sub-database.

A processor of an information processing apparatus that controls movement of a mobile object such as an automated guided vehicle or an autonomous mobile robot acquires an image in which the mobile object and the surrounding environment of the mobile object have been captured, acquires information of a position/posture of the mobile object, analyzes a motion of objects present in the surrounding environment of the mobile object based on a group of images acquired in a predetermined period of time, and determines control details with respect to movement of the mobile object based on the position information of the mobile object and the analysis result regarding the motion of the objects.

Provided are systems and methods for detecting a vehicle with sensors that are not calibrated properly and calibrating such sensor in real-time. In one example, a method may include iteratively capturing sensor data of a road while the vehicle is travelling on the road; monitoring a calibration of the sensors of the vehicle based on the sensor data, determining that the sensors of the vehicle are not calibrated properly based on the monitoring, generating a calibration target of an object on the road based on the sensor data, and adjusting a calibration parameter of the one or more sensors of the vehicle based on the generated calibration target.

In one embodiment, a vision aided navigation system comprises: at least one image sensor configured to produce image frames of a surrounding environment; a feature extractor configured to extract at least one image feature from a first image frame; a navigation filter configured to output a navigation solution based navigation data from a navigation device and changes in position of the image feature in the images; a feature tracker to receive the image frames and predict a location of the image feature in a subsequent image frame; a dynamic localized parameter adjuster to adjust at least one image parameter of the subsequent image frame; and wherein the feature tracker is configured so that when the image feature cannot be identified in the subsequent image frame within a bounded region around the predicted location, the dynamic localized parameter adjuster adjusts the at least one image parameter within the bounded region.

A method for determining extrinsic camera parameters includes: starting camera movement, capturing a first raw image with parallel first and second patches at a first point of time, and a second raw image with parallel third and fourth patches at a second point of time. A distance between the first and second patches and between the third and fourth patches is the same. A reference position A of a first patch image feature, a reference position C of a second patch image feature, an offset position B of the feature of the first patch in the third patch, and an offset position D of the feature of the second patch in the fourth patch are detected. An epipolar model is applied based on the positions A-D and a distance travelled by the camera between the first and second time points. Extrinsic camera parameters are determined from the epipolar model.

A method of annotating known objects in road images captured from a sensor-equipped vehicle, the method implemented in an annotation system and comprising: receiving at the annotation system a road image containing a view of a known object; receiving ego localization data, as computed in a map frame of reference, via localization applied to sensor data captured by the sensor-equipped vehicle, the ego localization data indicating an image capture pose of the road image in the map frame of reference; determining, from a predetermined road map, an object location of the known object in the map frame of reference, the predetermined road map representing a road layout the map frame of reference, wherein the known object is one of: a piece of road structure, and an object on or adjacent a road; computing, in an image plane defined by the image capture pose, an object projection, by projecting an object model of the known object from the object location into the image plane; and storing, in an image database, image data of the road image, in association with annotation data of the object projection for annotating the image data with a location of the known object in the image plane.

In various aspects, there is provided a system and method for determining a mediated reality positioning offset for a virtual camera pose to display geospatial object data. The method comprising: determining or receiving positioning parameters associated with a neutral position; subsequent to determining or receiving the positioning parameters associated with the neutral position, receiving positioning data representing a subsequent physical position; determining updated positioning parameters associated with the subsequent physical position; determining a updated offset comprising determining a geometric difference between the positioning parameters associated with a neutral position and the updated positioning parameters associated with the subsequent physical position; and outputting the updated offset.

Provided are systems and methods that invert a trained NeRF model, which stores the structure of a scene or object, to estimate the 6D pose from an image taken with a novel view. 6D pose estimation has a wide range of applications, including visual localization and object pose estimation for robot manipulation.

An image processing apparatus includes an image processing unit and a risk information generation unit. By processing at least one image, the image processing unit identifies a caution-requiring location being a location where a proximity indicator being an indicator related to a proximity state between a plurality of persons satisfies a criterion. The risk information generation unit outputs information indicating the caution-requiring location. For example, the information is displayed on a display unit through a display control unit. For each person, the image processing unit computes a first distance being the distance to the closest person to the person. Then, for each of a plurality of locations, the image processing unit counts the number of times the first distance has a reference value or less. Then, the proximity indicator is set by using the number of times the first distance has the reference value or less.

A system and a method of position and orientation determination use an image capturing device and position and orientation sensors in user equipment such as a head mounted device “HMD”. The method may comprise receiving position measurements from the position sensor and receiving a selection of one or more anchors based on said position measurements including the position of each anchor. The position and orientation measurements may be used to determine whether any selected anchor is visible to said image capturing device based on said position and orientation measurements. Then the image capturing device may be activated to capture an image including said one or more anchors when a selected anchor is visible. The image may be analyzed to determine the position and orientation of the image capturing device relative to the one or more anchors.