Aeva, Inc.

Apple Inc.

Mina A Rezk

Kumar Bhargav Viswanatha

Rajendra Tushar Moorti

Jose Krause Perin

Behsan Behzadi

Keith Gagne

Neal N Oza

Oguzhan Avci

Omer P Kocaoglu

Esha John

Pierre Hicks

Gautam Prabhakar

Kevin Pollock

Alexander Shpunt

Bruno Hexsel

Cristiano L Niclass

James Nakamura

Adrian Cort

Neeraj Tayal

Gennadiy A Agranov

Pradeep Srinivasan

Jean-Marie Bussat

Matthew C Waldon

Thierry Oggier

Andrew J Sutton

Krishna Toshniwal

Bryce Bradford

Richard E Bills

James Reuther

Ehsan Hamidi

Moshe Laifenfeld

Yuval Gerson

Shingo Mandai

Ronen Akerman

Tal Kaitz

Robert S Upton

Chandra Kakani

Jason D Mudge

Heethesh Vhavle Naresh

Ron Sokolovsky

Da An

Kshitij Jain

Chandra S Kakani

A light detection and ranging (LIDAR) system, includes a memory, and a processor, operatively coupled to the memory, to identify an obstruction of the LIDAR system based on a comparison of a frequency of an energy peak generated from a return signal to a threshold frequency and mitigate the obstruction.

Techniques for identifying obstructions in a LIDAR system

A light detection and ranging (LIDAR) system performs a method including generating a frequency domain waveform based on a baseband signal in a time domain, determining a first likelihood metric for frequencies in the frequency domain waveform, and identifying one or more frequencies in the frequency domain waveform that exceed a threshold value for the first likelihood metric. The method further includes determining a second likelihood metric for the frequencies in the frequency domain waveform, selecting a peak frequency from the frequency domain waveform corresponding to the frequency with the highest value for the second likelihood metric based on the one or more frequencies in the frequency domain waveform that exceed the threshold value for the first likelihood metric, and determining one or more properties of a target based at least in part on the selected peak frequency and the corresponding values of the first and second likelihood metrics.

Techniques for peak detection in a LIDAR system

A LiDAR system includes an optical source to emit an optical beam toward a target, a partially reflective surface to generate a local oscillator (LO) signal from the optical beam, and an optical lens disposed in front of a photodetector (PD), wherein the LO signal is incident at a decenter of the optical lens to shift the LO signal at the photodetector with respect to a return signal received from the target.

Techniques for FMCW LiDAR system descan compensation

A set of POIs of a point cloud are received at a first filter, where each POI of the set of POIs comprises one or more points. Each POI of the set of POIs is filtered. A set of neighborhood points of a POI is selected. A metric for the set of neighborhood points is computed based on a property of the set of neighborhood points and the POI, wherein the property comprises a velocity. Based on the metric, whether to accept the POI, modify the POI, reject the POI, or transmit the POI to a second filter, to extract at least one of range or velocity information related to the target is determined. Provided the POI is not accepted, modified, or rejected, the POI is transmitted to the second filter to determine whether to accept, modify, or reject the POI to extract the at least one of range or velocity information related to the target.

Techniques for filtering points in a light detection and ranging system

A method to compensate for phase impairments in a light detection and ranging (LIDAR) system includes estimating one or more phase impairments in the LIDAR system using a digitally-sampled reference signal to produce one or more estimated phase impairments and performing one or more corrections on one or more phase impairments in a digitally-sampled target signal based on the one or more estimated phase impairments.

Compensating for phase impairments in LIDAR systems

A method of compensation in a light detection and ranging (LIDAR) system. The method includes applying a first frequency shift to a target signal to compensate for doppler shift in the target signal and performing a phase impairment correction on the target signal to produce a corrected target signal. The method further includes undoing the first frequency shift on the corrected target signal.

Techniques for correcting phase impairments in a target signal

A method of determining range and velocity information of a target in a light detection and ranging (LiDAR) system, includes determining a set of signal power density spectrums in a frequency domain based on an optical signal returned from a target, wherein a number of the set of signal power density spectrums is determined based on an expected mirror Doppler shift associated with the optical signal returned from the target. The method further includes averaging the set of signal power density spectrums to generate a compensated signal power density spectrum and detecting a peak value in the compensated signal power density spectrum to determine range and velocity information related to a target based on a corresponding frequency of the peak value of compensated signal power density spectrum.

Techniques to use power spectrum density in coherent LiDAR systems

A method of operating a frequency-modulated continuous wave (FMCW) light detection and ranging (LIDAR) system is provided. The method includes transmitting a beam of co-propagating, cross-polarized light to a target. The method includes receiving return light reflected from the target by at least one detector. The method further includes transforming a polarization state of the beam at a transformation rate faster than a data collection rate from the at least one detector.

Techniques for performing speckle reduction using polarized light

A method generates first points based on a first scan of an environment that includes one or more moving objects. The method transforms the first points into a first static frame, which includes removing one or more of the first points corresponding to the one or more moving objects. The method generates second points based on a second scan of the environment that includes the one or more moving objects. The method transforms the second points into a second static frame, which includes removing one or more of the second points corresponding to the one or more moving objects. The method combines the first static frame and the second static frame into an accumulated static frame, which has an increase in resolution compared with the first static frame. The method then loads the accumulated static frame into a point cloud.

Techniques for point cloud frame accumulation in FMCW LIDAR

A method of operating a light detection and ranging (LiDAR) system is provided that includes performing a scene measurement using a LiDAR sensor capable of measuring Doppler per point. The method also includes estimating a velocity of the LiDAR sensor with respect to static points within the scene based on the scene measurement. The method may also include compensating for the velocity of the LiDAR sensor and compensating for a Doppler velocity of the LiDAR sensor.

Velocity estimation using doppler per point lidar systems

A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes a processor and a memory. The memory stores instructions that, when executed by the processor, cause the system to: generate subbands in a frequency domain based on a range-dependent time domain baseband signal, classify each subband into a subband type, select processing parameters for each subband based on the respective subband type, and process each of the subbands using the selected processing parameters for the subband.

Signal processing based on signal subband typing

Free-space optics for use in a light detection and ranging (LIDAR) apparatus include a polarization beam-splitter (PBS) to direct an optical beam in a first direction toward a target environment and to propagate a portion of the optical beam in a second direction for receipt by a photodetector (PD), a polarization wave plate (PWP) to convert the optical beam from a first polarization to a second polarization, and to convert the target return signal from a third polarization to a fourth polarization, and a lens system coupled between the PBS and the PWP to magnify the optical beam. The propagated portion of the optical beam comprises a local oscillator (LO) signal to mix with a target return signal to generate target information.

Techniques for processing a target return signal using free-space optics

A light detection and ranging (LIDAR) system is provided that includes a first optical source and a second optical source configured to emit respectively a first optical beam and a second optical beam that are nondegenerate and are chirped antiphase, lensing optics to direct the first and second optical beams toward a target, and collect a first return signal and a second return signal, and a first optical detector and a second optical detector configured to generate a first signal from the first return signal mixed with a first local oscillator and a second signal from the second return signal mixed with the second local oscillator.

Techniques for range and velocity measurements in a non-degenerate LiDAR system

A method includes transmitting a plurality of optical beams towards a plurality of targets, receiving a plurality of return signals based on reflections of the plurality of optical beams from the plurality of targets, and generating a first plurality of peaks each associated with a different up-chirp frequency of the plurality of optical beams and a second plurality of peaks each associated with a different down-chirp frequency of the plurality of optical beams. The method further includes determining peak shape similarities between each of the first plurality of peaks and the second plurality of peaks, pairing each peak of the first plurality of peaks with a peak of the second plurality of peaks based on the peak shape similarities, and identifying the plurality of targets based on the pairing.

Techniques to associate peaks in multi-target scenarios in coherent LIDAR systems

A light detection and ranging (LIDAR) system including a processor to receive a return signal from a target based on an optical beam transmitted towards the target and receive a baseband signal in a time domain based on the return signal. The processor of the LIDAR system further to produce a comparison of signal peaks of the baseband signal with an estimate of LIDAR system noise in the frequency domain, and identify targets based on the comparison.

LIDAR system target detection

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Los Gatos, CA, United States of America

Mina A Rezk