The invention is a method for detecting aberrant values of an incident wind field in a space located upstream of a lidar sensor. The method comprises acquiring and modelling a measurement rws(k) with the lidar sensor of an incident wind field, by estimating a median mr(k) and a mean absolute deviation dr(k) in real time of measurements of the incident wind field and detecting aberrant values in real time using the estimated median mr(k) and the mean absolute deviation dr(k).

A method and a system for detecting wire or wire-like obstacles, which method and system are designed for an aircraft. The system for detecting wire or wire-like obstacles comprises a detection device, such as a video camera or a LIDAR device, a computer and a display device. The method includes a step of detecting at least one pylon in the surrounding environment of the aircraft via a detection device, a step of identifying a family of pylons to which each detected pylon corresponds, a step of characterizing at least one cable supported by the at least one detected pylon, and a step of determining a prohibited zone that can potentially contain each pylon and each cable and a safe zone not containing either a pylon or a cable. The prohibited zone and the safe zone may be displayed on the display device.

Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.

A method for checking a calibration of N environmental sensors, wherein the N environmental sensors acquire an environment and each provide sensor data, N subfusions are formed from the acquired sensor data, each of the N subfusions leaves sensor data of one singular one of the N environmental sensors unconsidered upon the fusing, fusion results of the N subfusions are compared to one another, an incorrect calibration of the N environmental sensors is established based on a comparison result, and a check result is provided. Also disclosed are an associated device and a transportation vehicle.

In one embodiment, a system and method for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) is disclosed. The method selects poses of the point clouds that possess higher confidence level during the data capture phase as fixed anchor poses. The fixed anchor points are used to estimate and optimize the poses of non-anchor poses during point cloud registration. The method may partition the points clouds into blocks to perform the ICP algorithm for each block in parallel by minimizing the cost function of the bundle adjustment equation updated with a regularity term. The regularity term may measure the difference between current estimates of the poses and previous or the initial estimates. The method may also minimize the bundle adjustment equation updated with a regularity term when solving the pose graph problem to merge the optimized poses from the blocks to make connections between the blocks.

A computer-implemented method for occupancy state detection in an area for a pre-determined point in time. In aspects, the computer-implemented method includes operations carried out by computer hardware components. The operations include determining a probability distribution over a list of possible occupancy states of the area at a previous point in time, determining measurement data related to the area at the pre-determined point in time, and determining a probability distribution over the list of possible occupancy states of the area at the pre-determined point in time based on the measurement data and the probability distribution over the list of possible occupancy states of the area at the previous point in time.

A system for determining volume and flow characteristics for material on a conveyer belt is disclosed. The system includes an emitter, a sensor, and circuitry. The emitter is configured to generate radiation and direct the radiation toward a conveyer belt according to a field of view. The sensor is configured to measure reflected radiation from the conveyor belt and based on the generated radiation at a high framerate of about 20 to 30 Hertz and a high resolution of greater than about 4000 pixels and generate time of flight measurements. The circuitry is configured to generate time of flight measurements, determine three dimensional volume characteristics and flow characteristics for material conveyed by the conveyor belt using light detection and ranging based on the measured reflected radiation.

A laser measuring apparatus for measuring distances is disclosed, including a pulse laser; a photon detection device with a group of detection units; an evaluation device; a time measuring device; and a control device, wherein the control device is configured such that a plurality of measurement cycles is performed during each of the measurement operations; that one of the laser pulses is emitted with the pulse laser at the beginning of each measurement cycle of the plurality of measurement cycles; that, by means of the time measuring device, during each measurement cycle, one of the time periods is measured for each of the coincidence signals being detected during the respective measurement cycle; that the time periods measured during several of the measurement cycles of one of the measurement operations by means of the time measuring device are used to generate the measurement value of the respective measurement operation; that an adjustment of a maximum value for an event number takes place that corresponds to the number of time periods that are used during one of the measurement cycles to generate the measurement value of the respective measurement operation, wherein several of the time periods measured previously by means of the time measuring device are used for the adjustment; and that after the adjustment of the maximum value, the coincidence time is adjusted in dependence on the maximum value and a measurement value of a background radiation determined by the control device.

A method for Time-of-Flight (ToF) sensing of a scene is provided. The method includes performing, by a ToF sensor, a plurality of first ToF measurements using a first modulation frequency to obtain first measurement values. A respective correlation function of each of the plurality of first ToF measurements is periodic and exhibits an increasing amplitude over distance within a measurement range of the ToF sensor. The method additionally includes determining a distance to an object in the scene based on the first measurement values.

Systems and methods are disclosed for processing sparse tensors using a trained neural network model. An input sparse tensor may represent a sparse input point cloud. The input sparse tensor is processed using an encoder stage having a series of one or more encoder blocks, wherein each encoder block includes a sparse convolution layer, a sparse intra-channel attention module, a sparse inter-channel attention module, and a sparse residual tower module. Output from the encoder stage is processed using a decoder stage having a series of one or more decoder blocks, wherein each decoder block includes a sparse transpose convolution layer, a sparse inter-channel attention module, and a sparse residual tower module. The output of the decoder stage is an output sparse tensor representing a sparse labeled output point cloud.