A method and to a device for processing a 3D point cloud representing surroundings, which is generated by a sensor. Initially, starting cells are identified based on ascertained starting ground points within the 3D point cloud which meet at least one predefined ground point criterion with respect to a reference plane divided into cells. Thereafter, cell planes are ascertained for the respective starting cells of the reference plane. Thereafter, estimated cell planes and ground points are ascertained for candidate cells deviating from the starting cells based on the cell planes of the starting cells, which are subsequently converted into final cell planes. As a result of such a cell growth originating from the starting cells, the cells of the reference plane are iteratively run through and processed so that the 3D point cloud is reliably classifiable into ground points and object points based on this method.

An optical instrument for determining the distance to a target. The instrument includes a light source for emitting a pulsed light beam and a lens responsive to the light beam and projecting the light beam on the target. The instrument also includes an imaging lens responsive to a reflected beam from the projected light beam on the target and a TOF sensor including a photodetector array having an array of detector elements, where each detector element includes an FET switch and a capacitor for storing charge, and where the imaging lens focusing an image of the projected light beam on a group of the detector elements in the array. Processing electronics control the light source and processing of the image of the projected beam on the array, where the processing electronics determine a time from when the light beam is emitted and the image of the projected beam is created.

A method for point cloud compression of an intelligent cooperative perception (iCOOPER) for autonomous air vehicles (AAVs) includes: receiving a sequence of consecutive point clouds; identifying a key point cloud (K-frame) from the sequence of consecutive point clouds; transforming each of the other consecutive point clouds (P-frames) to have the same coordinate system as the K-frame; converting each of the K-frame and P-frames into a corresponding range image; spatially encoding the range image of the K-frame by fitting planes; and temporally encoding each of the range images of the P-frames using the fitting planes.

A LIDAR system is described for detecting surroundings, including a laser light source for emitting a laser light, a receiving device for receiving a laser light reflected by the surroundings, and a control device for activating the laser light source, the control device being configured to activate the laser light source for emitting a continuous light beam and to continually modulate the emitted light beam, so that the light beam includes a multitude of successive codes.

A calibration method implemented by a computer, includes: measuring, with a laser ranging sensor, markers attached to at least two predetermined positions of a bed portion of a trampoline and calculating coordinates of the markers in a first coordinate system with a position of the laser ranging sensor being an origin; and calculating a conversion parameter to convert coordinates of respective positions of the first coordinate system into coordinates of respective positions of a second coordinate system with a center position of the bed portion being an origin based on a relationship between the calculated coordinates of the markers and the at least two predetermined positions of the bed portion.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system 104 employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system 104's receiver 304 is saturated. As such, the radar system 104 can forgo integrating an automatic gain control circuit to prevent the receiver 304 from becoming saturated. Furthermore, the radar system 104 can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system 104's dynamic range increases, which enables the radar system 104 to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system 104.

A recording medium records a program that causes a computer to execute processing of: obtaining pieces of three-dimensional point data observed a mobile body; calculating a feature amount according to a distance between a position of the mobile body and each of three-dimensional points indicated by the pieces of three-dimensional point data; and specifying a reference point that corresponds to the position of the mobile body from among reference points in a mobile space of the mobile body which are registered in a database that registers a combinations each of the respective reference points and the feature amounts according to the distance between the position of the respective reference points and each of three-dimensional points indicated by a pieces of three-dimensional point data observed from the position of the respective reference points, based on the calculated feature amount and the database.

A method for optical distance measurement is proposed which comprises the emission of a plurality of measurement pulses, the reflection of emitted measurement pulses at at least one object and the receipt of reflected measurement pulses. A sequence of measurement pulses is emitted, wherein the sequence comprises temporal pulse spacings between temporally successive measurement pulses, and wherein each measurement pulse of the sequence has a temporal pulse width of T(Pulse). The pulse spacings form a first set, wherein the first set is defined by {T(delay)+i*T(Pulse): i is an element of the natural numbers between 0 and j}, wherein for all values of i it holds that: T(delay)+i*T(Pulse)<(2T(delay)+2T(Pulse)), wherein the first set only comprises one element for all values of i between 0 and j, respectively, and wherein T(delay) defines a pulse spacing base unit.

A system and method of LIDAR imaging to overcome scattering effects pulses a scene with light pulse sequences from a light source. Reflected light from the scene is measured for each light pulse to form a sequence of time-resolved signals. Time-resolved contrast is calculated for each location in a scene. A three-dimensional map or image of the scene is created from the time-resolved contrasts. The three-dimensional map is then utilized to affect operation of a vehicle.

Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.