A system is presented in accordance with aspects of the present disclosure. In various embodiments, the system includes a light source configured to emit light, an emitting lens positioned to obtain the emitted light and configured to produce a shaped beam, an optical element positioned to obtain the shaped beam and redirect the shaped beam toward a near field object to produce scattered light from the near field object, and to obtain and redirect at least a portion of the scattered light, and a collection lens configured to focus the at least the portion of the scattered light on a light detector.

The invention relates to a method for determining an occupancy status of a parking bay, wherein a vehicle is moved along a street segment with at least one parking bay and the vehicle has a distance sensor as well as a sensor for satellite-based location and time determination. Furthermore, static parking information on the position of the parking bays in a street segment and parking information on the parking bays are on hand. By means of distance and location data projected distance data are produced which each indicate a distance datum of the distance sensor to the next object in the sensor direction at a point of the street of the street segment at the parking bay. By means of these projected distance data a standard distance is ascertained. Subsequently, for each point of a parking bay the determination is made if this is unoccupied by comparing the distance datum with the standard distance plus a delta. An area of a parking bay is determined as one or several vacant parking spaces if the rounded-down quotient of the length of the adjoining contiguous unoccupied points to the length of an average parking space is 1 or greater.

A method for calibrating a sensor unit disposed on a load-bearing device of an industrial truck includes the steps of: determining a first position of the sensor unit relative to an object located remotely from the industrial truck, displacing the sensor relative to the object in a first direction by a first distance, determining a second position of the sensor unit relative to the object, determining the spatial position or arrangement of the sensor unit relative to the load-bearing device based on the first and second positions, the direction of movement, and the distance between the first and second positions.

A computer implemented scheme for a light detection and ranging (LIDAR) system where point cloud feature extraction and segmentation by efficiently is achieved by: (1) data structuring; (2) edge detection; and (3) region growing.

Provided is an object recognition device for performing object recognition on a field of view (FoV). The object recognition device includes a light detection and ranging (LiDAR) data acquisition module configured to acquire data for the FoV from a sensor configured to project the FoV with a laser and receive reflected light, and a control module configured to perform object recognition on an object of interest in the FoV using an artificial neural network, wherein the control module includes a region of interest extraction module configured to acquire region of interest data based on acquired intensity data for the FoV, and an object recognition module configured to acquire object recognition data using an artificial neural network, and recognize the object of interest for the FoV.

In a distance measurement device, a control unit performs a charge distribution process in which in a first period, charge generated in a charge generation region is transferred to a first charge storage region and, in a second period, the charge generated in the charge generation region is transferred to a second charge storage region. The control unit applies an electric potential to a first overflow gate electrode so that a potential energy of a region immediately below the first overflow gate electrode is lower than a potential energy of the charge generation region in the first period, and applies an electric potential to a second overflow gate electrode so that a potential energy of a region immediately below the second overflow gate electrode is lower than a potential energy of the charge generation region in the second period.

The present disclosure discloses a path planning method and device and a mobile device. The method comprises: collecting environmental information in a viewing angle by a sensor of a mobile device, processing the environmental information by using an SLAM algorithm, and constructing a grid map; dividing the grid map to obtain a plurality of pixel blocks, using an area constituted of pixel blocks not occupied by obstacles as a search area for path planning, and obtaining a processed grid map; determining reference points by using pixel points in the search area, and deploying topological points on the processed grid map according to the reference point determined and constructing a topological map; and calculating an optimal path from a starting point to a preset target point by using a predetermined algorithm according to the topological map constructed. The present disclosure improves path planning efficiency and saves storage resources.

A method of detecting a lane line based on lidar data can include detecting, by a processor, points each estimated as a lane line in a lidar data, performing, by the processor, an estimation operation of estimating parameters of a mathematical model using the detected points, and performing, by the processor, a setting operation of calculating distances between each of the detected points and the mathematical model in which the parameters are estimated and setting the calculated distances as scores. The method can further include performing, by the processor, a summation operation of summing the scores, and setting, by the processor, the mathematical model determined according to the summation score as a lane line.

A system and a method for performing exchange of lidar datapoints between a lidar sensor and a host computing device are provided. The lidar sensor stores a sensor payload size range, and the host computing device stores a host payload size range. When the lidar connection is initiated, the host computing device and the lidar sensor perform handshaking to determine an overlapping range between the host sensor payload size range and the sensor payload size range, and to negotiate an acceptable datapoint payload size based on the overlapping range. Once the handshaking is complete, the lidar connection may be performed using the acceptable data point payload size as a payload size thereof. Thus, the payload size is independent of a maximum transmission unit (MTU) of an Ethernet.

The subject matter of this specification can be implemented in, among other things, systems and methods of optical sensing that utilize optimized processing of multiple sensing channels for efficient and reliable scanning of environments. The optical sensing includes multiple optical communication lines that include coupling portions configured to facilitate efficient collection of various received beams. The optical sensing system further includes multiple light detectors configured to process collected beams and produce data representative of a velocity of an object that generated the received beam and/or a distance to that object.