A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

A method is described for the in particular optical capture of at least one object (18, 20) with at least one sensor apparatus (14) of a vehicle (10), a device (34) of a sensor apparatus (14), a sensor apparatus (14) and a driver assistance system (12) with at least one sensor apparatus (14). In the method, in particular optical transmitted signals (36) are transmitted into a monitoring region (16) with the at least one sensor apparatus (14) and transmitted signals (36) reflected from object points (40) of the at least one object (18, 20) are captured as received signals (38) with angular resolution with reference to a main monitoring direction (42) of the at least one sensor apparatus (14). A spatial distribution of the object points (40) of the at least one object (18, 20) relative to the at least one sensor apparatus (14) is determined from a relationship between the transmitted signals (36) and the received signals (38), and the at least one object (18, 20) is categorized as stationary or non-stationary. A spatial density of the captured object points (40) in at least one region of the at least one object (20) is determined, and if the density of the captured object points (40) is smaller than a predetermined or predeterminable threshold value, the at least one object (20) is categorized as stationary.

A method and an apparatus optimizes scan data obtained by sensors on vehicle, and corrects trajectory for a vehicle/robot based on the optimized scan data. The method for optimizing the scan data obtained by scanning environment elements, includes: step of obtaining the scan data, including obtaining at least two frames of scan data respectively corresponding to different timings; step of cluster processing, based on the characteristic of the data points, including classifying the plurality of data points in each frame of the scan data into one or more clusters; step of establishing correspondence, among the at least two frames of scan data, including searching and obtaining at least one set of clusters having correspondence; step of optimizing clusters, among the at least two frames of scan data, including conducting calculation to each set of the at least one set of clusters having correspondence, to obtain optimized clusters respectively corresponding to each set of the at least one set of clusters having correspondence; and step of optimizing the scan data, including accumulating all optimized clusters to obtain an optimized scan date for the at least two frames of scan data.

A method comprises accessing a data set comprising a LIDAR acquired point cloud comprising a plurality of points each of which are attributed with at least a geospatial coordinate, sub-sampling at least a portion of the plurality of points to derive a representative sample of the plurality of points and displaying the representative sample of the plurality of points.

An object monitoring system includes a distance measuring device which generates, while repeating an imaging cycle having different imaging modes, a distance image of a target space for each of the imaging modes, and a computing device which determines presence or absence of an object within a monitoring area set in the target space based on the distance image.

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.

Systems and methods for object detection in a dusty environment can enhance the ability of autonomous machines to distinguish dust clouds from solid obstacles and proceed appropriately. A library of dust classifiers can be provided, where each dust classifier is separately trained to distinguish airborne dust from objects in the environment. Different dust classifiers can correspond to different categories of dusty environments. Based on current conditions, control logic in an autonomous machine can categorize its environment and select a corresponding dust classifier. The dust classifier output can be used to alter a behavior of the autonomous machine, including a behavior of the control logic. For instance, the control logic can apply a consistency check to the output of the dust classifier and an output of an AI-based object classifier to detect instances where the object classifier misidentifies dust as an object.

A LiDAR system including a laser amplification system is disclosed. The laser amplification system includes a laser source and an optical amplifier. The laser source has a laser cavity and is configured to output electromagnetic radiation. The optical amplifier includes quantum dots and is positioned outside the laser cavity to receive the electromagnetic radiation output from the laser source. The optical amplifier amplifies the received electromagnetic radiation using the quantum dots and outputs the amplified electromagnetic radiation.

According to various embodiments, a solid-state light detection and ranging (LiDAR) transmitter includes a tunable optical metasurface to selectively steer incident optical radiation long an azimuth axis. In some embodiments, different subsets of lasers in an array of lasers are activated to generate optical radiation for incidence on the metasurface at different angles of incidence on an elevation axis for unsteered deflection by the metasurface at corresponding angles of elevation. In some embodiments, a prism is positioned relative to the tunable optical metasurface to deflect the optical radiation from the optical assembly by the optical radiation source for incidence on the metasurface at an angle of incidence that is between the first steering angle and the second steering angle, such that the optical radiation incident on the metasurface and the steered output optical radiation from the metasurface spatially overlap within the prism.

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.