Abstract: An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization. The one or more processors are configured to calculate a value of reflectivity based on a signal-to-noise ratio (SNR) value of the first polarized signal and an SNR value of the second polarized signal. The one or more processors are configured to operate a vehicle based on the value of reflectivity.

Abstract: A system and method for combining multiple functions of a light detection and ranging (LIDAR) system includes receiving a second optical beam generated by the laser source or a second laser source, wherein the second optical beam is associated with a second local oscillator (LO); splitting the second optical beam into a third split optical beam and a fourth split optical beam; transmitting, to the optical device, the third split optical beam and the fourth split optical beam; receiving, from the optical device, a third reflected beam that is associated with the third split optical beam and a fourth reflected beam that is associated with the fourth split optical beam; and pairing the third reflected beam with the second LO signal and the fourth reflected beam with the second LO signal.

Abstract: A light detection and ranging (LIDAR) system includes one or more components that include at least one of an electrical circuit, an electro-optical component, or an optical component. The one or more components are configured to receive an optical beam generated by a laser source, split the optical beam into a plurality of optical beams, transmit the plurality of optical beams through a first subset of optical paths. The one or more components are configured to in response to transmitting the plurality of optical beams, receive a reflected beam through a second subset of the optical paths, generate a first output signal based on a first local oscillator (LO) signal and the reflected beam, and generate a second output signal based on a second local oscillator (LO) signal and the reflected beam.

Abstract: A light detection and ranging (LIDAR) system includes a transceiver, a laser source coupled to the transceiver, a time-division multiplexing (TDM) circuit, and one or more processors. The TDM circuit is configured to generate a plurality of first signals. The one or more processors are configured to control the laser source to provide an optical beam to a first input optical channel of a plurality of input optical channels of the transceiver during a first time slot, based on the optical beam provided to the first input optical channel, control the transceiver to send a first reflected beam and a second reflected beam to the TDM circuit through a first output optical channel of a plurality of output optical channels of the transceiver, and based on a control signal provided to the TDM circuit, control the TDM circuit to select the plurality of first signals during the first time slot.

Abstract: An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization. The one or more processors are configured to calculate a value of reflectivity based on a signal-to-noise ratio (SNR) value of the first polarized signal and an SNR value of the second polarized signal. The one or more processors are configured to operate a vehicle based on the value of reflectivity.

Abstract: An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization that is orthogonal to the first polarization. The one or more processors are configured to operate a vehicle based on a ratio of reflectivity between the first polarized signal and the second polarized signal.

Abstract: A system and method for combining multiple functions of a light detection and ranging (LIDAR) system includes receiving a second optical beam generated by the laser source or a second laser source, wherein the second optical beam is associated with a second local oscillator (LO); splitting the second optical beam into a third split optical beam and a fourth split optical beam; transmitting, to the optical device, the third split optical beam and the fourth split optical beam; receiving, from the optical device, a third reflected beam that is associated with the third split optical beam and a fourth reflected beam that is associated with the fourth split optical beam; and pairing the third reflected beam with the second LO signal and the fourth reflected beam with the second LO signal.

Abstract: Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene.

Abstract: Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene.

Abstract: Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene.