A method to compensate for phase impairments in a light detection and ranging (LIDAR) system includes transmitting a first optical beam towards a target, receiving a second optical beam from the target to produce a received optical beam; and generating a digitally-sampled target signal using a local oscillator (LO) beam, a first photo-detector and the received optical beam. The method also includes generating a digitally-sampled reference signal using a reference beam transmitted through a fiber delay device and a second photo-detector, and estimating one or more phase impairments in the LiDAR system using the digitally-sampled reference signal to produce one or more estimated phase impairments. The method also includes performing a first correction on a first phase impairment introduced into the digitally-sampled target signal by the LO beam; performing a second correction on a second phase impairment introduced into the digitally-sampled target signal by the received optical beam.

A light detection and ranging (LIDAR) technique that includes dividing the field of view into a grid including a plurality of cells. The technique also includes generating a baseband signal based on a returned optical beam. The baseband signal includes a plurality of peaks corresponding with up-chirps and down-chirps in the transmitted signal. A plurality of points are computed based on the peaks. Each point includes information describing a range and a velocity and corresponds to a respective cell. A point confidence score is computed for each point, and a cell confidence score is computed for each cell based on the point confidence scores of the points within the cell. Each point can be accepted or rejected for inclusion in a point cloud based on the point confidence score for the point and the cell confidence scores for the plurality of cells.

A method for detecting an operating terrain is provided. The method includes obtaining point cloud data of an operating region that are collected by a laser radar at a current time, including three-dimensional coordinates of a plurality of sampling points. The operating region is divided into a plurality of grids, each having a corresponding height value. The method includes for any grid determining an input point of the grid from the plurality of sampling points, based on the three-dimensional coordinates. The method includes determining a type of the input point, based on a height coordinate of the input point and the height value of the grid. The type includes a noise point and a ground point. The method includes in response to determining that the input point is the ground point, updating the height value of the grid based on the height coordinate of the input point.

Techniques for identifying curbs are discussed herein. For instance, a vehicle may generate sensor data using one or more sensors, where the sensor data represents points associated with a driving surface and a sidewalk. The vehicle may then quantize the points into distance bins that are located laterally along the driving direction of the vehicle in order to generate spatial lines. Next, the vehicle may determine separation points for the spatial lines, where the separation points are configured to separate the points associated with the driving surface from the points associated with the sidewalk. The vehicle may then generate, using the separation points, a curve that represents the curb between the driving surface and the sidewalk. This way, the vehicle may use the curve while navigating, such as to avoid the curb and/or stop at a location that is proximate to the curb.

Provided herein is a method, apparatus, and computer program product for identifying locations along a road segment as a tunnel based on point cloud data. Methods may include: receiving point cloud data representative of an environment of a trajectory along a road segment; generating, from the point cloud data, one or more two-dimensional images in one or more corresponding planes orthogonal to the trajectory; determining, for the one or more two-dimensional images, a probability as to whether a respective two-dimensional image is captured within a tunnel along the road segment; and classifying a point along the road segment at a position corresponding to a respective one of the one or more two-dimensional images as a tunnel point in response to the probability as to whether the respective two-dimensional image is captured within a tunnel along the road segment satisfying a predetermined value.

A LIDAR 1 includes: a scanner 55 that emits outgoing light Lo while changing the outgoing direction thereof; a reflection member 8 that is arranged in a first outgoing direction and reflects the outgoing light Lo; an absorption member 7 that is arranged in a second outgoing direction and absorbs the outgoing light Lo; an APD 41 that receives return light Lr; and a DSP16. The DSP 16 generates replica u representing a component reflected by the absorption member 7 on the basis of output signals of the APD 41 obtained at each time when the outgoing light Lo is emitted in the first outgoing direction and in the second outgoing direction.

A multi-line Lidar is provided. The multi-line Lidar includes: a multi-line ranging laser emission module comprising one or more lasers; a multi-line ranging laser reception module comprising one or more photodetectors and adapted to detect a laser echo generated when a measurement laser emitted by the laser emission module is incident to an obstacle and is diffusedly reflected; a ranging information resolution module in electrical signal connection with the multi-line ranging laser emission module and the multi-line ranging laser reception module, and designed to calculate the distance, in each direction, to the obstacle by means of calculating the time difference between the emission of the measurement laser and the receiving of the laser echo; and a control circuit and an optical system correspondingly configured for the multi-line ranging laser emission module and the multi-line ranging laser reception module.

Three-dimensional (3D) point cloud generation using a stationary laser scanner and a mobile scanner. The method includes scanning a first part of a surrounding with the stationary laser scanner, obtaining a first 3D point cloud, scanning a second part of the surrounding with the mobile scanner, obtaining a second 3D point cloud, whereby there is an overlap region of the first part and the second part, and aligning the second 3D point cloud to the first 3D point cloud to form a combined 3D point cloud. The positional accuracy of points of the second 3D point cloud is increased by automatically referencing second scanner data of the overlap region, generated by the mobile scanner, to first scanner data of the overlap region, generated by the stationary laser scanner. Therewith, deformations of the second 3D point cloud and its alignment with the first 3D point cloud are corrected.

A distance measuring unit for measurement, based on signal time of flight, of a distance to an object, includes: an emitter configured for the emission of electromagnetic pulses, and sequentially into different emitter solid angle segments of the detection field, a receiver having a first face for detecting electromagnetic radiation, and imaging optics which image the detection field onto the first sensor face, and specifically each of the emitter solid angle segments onto a respective region of the first sensor face. The emitter solid angle segments follow one another along a scan axis, and correspondingly the regions of the first sensor face also follow one another along a first scan line. The first sensor face is subdivided into at least two pixels which adjoin one another on a first separating line. The first separating line extends at least in sections obliquely with respect to the first scan line.

Methods and devices for determining axis of symmetry of self-driving vehicle (SDV) and for calibrating a Light Detection and Ranging (LIDAR) system are disclosed. One of the axes of the system of coordinates of the LIDAR system extends along a normal direction of a ground surface. the method includes acquiring a subset of detected points in the system of coordinates; generating a subset of mirror-image points based on the subset of detected points; projecting the subset of mirror-image points onto the subset of detected points so as to define pairs of overlapping data points; using symmetrically opposite detected points for determining the axis of symmetry of the SDV in the system of coordinates of the LIDAR system; and calibrating the LIDAR system using an angular offset between the axis of symmetry of the SDV and an other one of the axes of the system of coordinates of the LIDAR system.