A method for providing assistance in aiming of one or more illumination devices in an area may include, by a processor, receiving photometric data for an area, and using the photometric data to determine an aiming vector for the illumination device. The area may include the illumination device. the method may further include receiving, from an orientation sensor module of the illumination device, orientation data for the illumination device, and using the orientation data and the aiming vector to determine if there is an error in the aiming of the illumination device. The response to determining that there is an error in the aiming of the illumination device, the method further includes causing a controller associated with a driving means of the illumination device for correcting the error in the aiming of the illumination device.

The present invention provides a method for calculating a corrected substrate height map of a first substrate using a height level sensor. The method comprises: sampling the first substrate by means of the height level sensor with the first substrate moving with a first velocity, wherein the first velocity is a first at least partially non-constant velocity of the first substrate with respect to the height level sensor, to generate a first height level data, generating a first height map based on the first height level data, and calculating a corrected substrate height map by subtracting a correction map from the first height map, wherein the correction map is calculated from the difference between a first velocity height map and a second velocity height map.

The present invention provides a method for calculating a corrected substrate height map of a first substrate using a height level sensor. The method comprises: sampling the first substrate by means of the height level sensor with the first substrate moving with a first velocity, wherein the first velocity is a first at least partially non-constant velocity of the first substrate with respect to the height level sensor, to generate a first height level data, generating a first height map based on the first height level data, and calculating a corrected substrate height map by subtracting a correction map from the first height map, wherein the correction map is calculated from the difference between a first velocity height map and a second velocity height map.

Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.

Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.

Systems, devices, methods, and computer-readable media for. A method can include receiving, from a laser scan device of a tolling station, a time series of distance measurements, determining, based on the time series of distance measurements, height measurements indicating a height of a vehicle from a surface of a road. generating, based on the height measurements, an image of the height measurements, and classifying, using the image as input to a convolutional neural network (CNN), the vehicle.

Systems, devices, methods, and computer-readable media for. A method can include receiving, from a laser scan device of a tolling station, a time series of distance measurements, determining, based on the time series of distance measurements, height measurements indicating a height of a vehicle from a surface of a road. generating, based on the height measurements, an image of the height measurements, and classifying, using the image as input to a convolutional neural network (CNN), the vehicle.

Apparatus and associated methods relate to a smart hook, a safety harness module (125), and associated electronic components that detect a safety state of a user by monitoring various parameters at the smart hook and safety harness module (125) and determining whether the user is using proper safety protocol at extreme heights and/or whether the user has experienced a height-related accident. In an illustrative example, the user may don a safety harness (115) that may include a module (125) that contains sensors that monitor an acceleration/velocity/position of the user and/or ambient air pressure around the user. The module (125) may receive wireless signals from at least one rebar hook (120a, 120b) having sensors that monitor the acceleration/velocity/position and gate position of the rebar hooks (120a, 120b). A controller included with the safety harness module (125) may use these sensors to advantageously determine the safety state of the user and generate alert/warning signals.

Apparatus and associated methods relate to a smart hook, a safety harness module (125), and associated electronic components that detect a safety state of a user by monitoring various parameters at the smart hook and safety harness module (125) and determining whether the user is using proper safety protocol at extreme heights and/or whether the user has experienced a height-related accident. In an illustrative example, the user may don a safety harness (115) that may include a module (125) that contains sensors that monitor an acceleration/velocity/position of the user and/or ambient air pressure around the user. The module (125) may receive wireless signals from at least one rebar hook (120a, 120b) having sensors that monitor the acceleration/velocity/position and gate position of the rebar hooks (120a, 120b). A controller included with the safety harness module (125) may use these sensors to advantageously determine the safety state of the user and generate alert/warning signals.

The purpose of this invention is to reduce the ambiguity in position caused by height differences in an object, and to estimate the position of an object with a high degree of accuracy. This object position estimation device comprises: a first processing unit which detects the position of a position reference point on a moving object from an image of the moving object obtained by a camera; a second processing unit for estimating the height of the moving object detected; a third processing unit for estimating the height of the position reference point on the basis of the estimated height estimated by the second processing unit and the image of the moving object; a fourth processing unit which calculates an estimated position candidate for the moving object on the basis of the height of the position reference point estimated by the third processing unit, the position of the position reference point, and the height of the point in the area; a fifth processing unit which calculates the likelihood of the estimated position candidate on the basis of the estimated position candidate calculated by the fourth processing unit, the height of the position reference point estimated by the third processing unit, and the height in the area; and a sixth processing unit which determines the estimated position of the moving object on the basis of the likelihood of the estimated position candidate calculated by the fifth processing unit.