A distance measuring light projecting module comprises a light receiving module for receiving a reflected distance measuring light and a background light, a distance measuring unit for receiving the reflected distance measuring light and performs a distance measurement, an image pickup module for receiving the background light and for acquiring a background image, an optical axis deflector for integrally deflecting an optical axis of the distance measuring light and an optical axis of the background light, and an arithmetic control module for controlling the optical axis deflector, wherein the optical axis deflector has a rotary driving module for rotating a pair of disk prisms individually, and a projecting direction detecting module for detecting a rotation angle of each of the disk prisms.

A frequency-modulated continuous wave (FMCW) LIDAR can be configured to reduce re-reflection and cross-coupling in the FMCW LIDAR. A first laser can be configured to generate a ranging signal, and a second laser can be configured to generate a local oscillator signal. A feedback control can be configured to maintain an offset between the ranging signal and the local oscillator signal. The offset can be a non-zero value. A transmit portion configured to emit a reference laser signal based on the ranging signal into an environment. A receiver portion can be configured to receive a return laser signal from the environment. The return laser signal can be a reflected version of the reference laser signal. A receiver photodetector can be configured to combine the return laser signal and the local oscillator signal.

A free space detection apparatus for a vehicle is provided to detect a free space based on a lidar sensor and recognize a driving environment. The apparatus includes a point data processor that removes noise of point data acquired from the LIDAR sensor and derives a half line with respect to an obstacle point from which noise has been removed based on coordinates of the obstacle point. A map processor generates a map for space classification, checks whether the obstacle point or the half line with respect to the obstacle point is present in a cell of the map and determines cell states of the map. A free space detector detects a free space based on the cell states of the map.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a room of the home based upon processor analysis of the LIDAR data; builds a 3D model of the room based upon the measured plurality of dimensions; receives an indication of a proposed change to the room; modifies the 3D model to include the proposed change to the room; and displays a representation of the modified 3D model.

A target acquisition system includes a transmitter configured to emit a plurality of pulses at a plurality of transmit times toward a target, a receiver configured to detect a plurality of photon arrival events at a plurality of receive times, and a processor configured to determine a range of the target and a range-rate of the target by identifying a subset of the receive times and a subset of the transmit times, generating scaled transmit times based on the subset of the transmit times and a plurality of trial target velocities relative to the receiver, cross-correlating the scaled transmit times and the subset of the received times to generate a plurality of cross-correlation power values, and calculating the range and the range-rate of the target based on the plurality of cross-correlation power values.

An optical sensing device includes a light source, which emits one or more beams of light pulses toward a scene. An array of single-photon detectors output electrical pulses in response to photons that are incident thereon. Light collection optics form an image of the scene on the array. Processing circuitry counts the electrical pulses output by the single-photon detectors during multiple time intervals following each of the light pulses, detects, responsively to the counted pulses, an object located less than 10 cm away from the array, makes a comparison between respective counts of the electrical pulses output by the single-photon detectors group during a specified time interval immediately following each of a plurality of the light pulses, and ascertains, responsively to the comparison, whether the object reflecting the at least one of the beams is fixed to the device or separate from the device.

Techniques for determining occupancy using unobstructed sensor emissions. For instance, a vehicle may receive sensor data from one or more sensors. The sensor data may represent at least locations to points within an environment. Using the sensor data, the vehicle may determine areas within the environment that are obstructed by objects (e.g., locations where objects are located). The vehicle may also use the sensor data to determine areas within the environment that are unobstructed by objects (e.g., locations where objects are not located). In some examples, the vehicle determines the unobstructed areas as including areas that are between the vehicle and the identified objects. This is because sensor emissions from the sensor(s) passed through these areas and then reflected off of objects located farther distances from the vehicle. The vehicle may then generate a map indicating at least the obstructed areas and the unobstructed areas within the environment.

A mapping system, comprising an inertial measurement unit; a camera unit; a laser scanning unit; and a computing system in communication with the inertial measurement unit, the camera unit, and the laser scanning unit, wherein the computing system computes first measurement predictions based on inertial measurement data from the inertial measurement unit at a first frequency, second measurement predictions based on the first measurement predictions and visual measurement data from the camera unit at a second frequency and third measurement predictions based on the second measurement predictions and laser ranging data from the laser scanning unit at a third frequency.

Examples of a three-dimensional (3D) optical sensing system for a vehicle include a modular architecture. Light can be transmitted to an optical signal processing module, which can include a photonic integrated circuit (PIC) that can create one or more signals with tailored amplitude, phase, and spectral characteristics. The plurality of optical signals processed by the optical signal processing module can be sent to beam steering units distributed around the vehicle. The steering units can direct a plurality of optical beams towards targets. The return optical signal can be detected by a receiver PIC including an array of sensors and using a direct intensity detection technique or a coherent detection technique. The return optical signal can be converted into an electrical signal by the array of sensors, which can then be processed by the electronic signal processing unit, and information about the location and speed of the targets can be quantified.