A display apparatus includes a display screen, and a controller that causes the display screen to display a composite image in which a first image acquired by imaging a space by a camera and a second image representing at least one type of aerosol existing in the space are combined. The position of the at least one type of aerosol as seen in a depth direction in the first image is reflected in the second image.

Systems and methods for Geiger-mode laser vibrometry are described. An example method for laser vibrometry includes receiving a first time-series of single photon arrivals corresponding to a laser beam reflected from or transmitted through a target, the single photon arrivals including information corresponding to vibrations of the target, each single photon arrival separated in time from another single photon arrival, determining, based on two or more of the single photon arrivals, a discrete time sequence having a binary value, and generating a second time-series by assigning a non-binary value to each of the discrete time points, wherein each of the assigned non-binary values is determined based on a number of discrete time points lacking a photon arrival prior to receiving a photon.

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

A LIDAR device is positioned on a crane and is moved along a track to collect 3D image data of the area below the crane. The resulting image data is sent to a computing device that applies or more filtering algorithms to the image data and searches for known image shapes therein through a comparison of the image data and one or more 3D object models based on known shapes or geometric primitives. If an object is identified in the image data and is determined to be accessible, position information for that object may be sent to a device configured to control movement of the crane to grab/pick up the object.

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.

The present disclosure relates to light detection and ranging (lidar) systems, lidar-equipped vehicles, and associated methods. An example method includes causing a firing circuit to trigger emission of an initial group of detection pulses from at least one light-emitter device of a lidar system in accordance with an initial set of one or more light-emission parameters. The method also includes causing the firing circuit to trigger emission of one or more test pulses and receiving, from at least one detector, information indicative of one or more return test pulses. The method yet further includes determining, based on the received information, a presence of a retroreflector based on an intensity of the return test pulse. The method additionally includes determining a subsequent set of light-emission parameters and causing the firing circuit to trigger emission of a subsequent group of detection pulses in accordance with the subsequent set of light-emission parameters.

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.

A device, a memory medium, a computer program, and a computer-implemented method for validating a data-based model for classifying an object into a class for an object type or a function type for a driver assistance system of a vehicle. The classification is determined as a function of a digital signal using the data-based model. A reference classification for the object is determined as a function of the digital signal, using a reference model. It is checked, as a function of the classification and the reference classification, whether or not the classification of the data-based model for the object is correct, and the data-based model is validated or not validated, depending on whether or not the classification is correct. The classification and the reference classification are determined for a set of digital signals that are associated with different distances between the object and a reference 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.