Methods and Apparatus for Increased Precision and Improved Range in a Multiple Detector LiDAR Array

• US 20160259038A1
• Filed: 03/05/2015
• Published: 09/08/2016
• Est. Priority Date: 03/05/2015
• Status: Active Grant

• Current Assignee: Big Sky Financial Corporation
• Original Assignee: Facet Technology Corp.
• Inventors: Retterath, James E., Laumeyer, Robert A.
• Granted Patent: US 10,036,801 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 17/10 using transmission of interrupted, pulse-modulated waves determination of distance by phase measurements G01S17/32
• CPC Class Codes: G01S 17/894 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/4815 using multiple transmitters
• CPC Class Codes: G01S 7/4865 Time delay measurement, e.g. time-of-flight measurement, time of arrival measurement or determining the exact position of a peak peak detection in noise, signal conditioning G01S7/487

Abstract

Methods, systems, and computer program products for acquiring three-dimensional LiDAR information of a scene are disclosed. According to one aspect, acquiring three-dimensional information includes emitting N pulses in a sequence with each successive pulse having a relative time shift to the sampling reference, thus producing a reconstructed sampled signal with an effective sampling rate of N times the sampling reference. According to another aspect, acquiring three-dimensional information includes emitting two or more frequencies, the differences of each pair of differing frequencies being designated as Δf, and sampling the return information with the use of a sampling reference. Frequency analysis is performed on the sampled information to determine the reference times at which the Δf signals occur and the signal intensity of the Δf signals at each time. Systems as described herein can be utilized for autonomous vehicle navigation, collision avoidance and navigation systems for UAVs, roadway surface texture analysis, non-contact friction analysis, and in-motion deflectometer measurement.

Claims

1. A light detecting and ranging (LiDAR) system for acquiring information about objects in a scene, the LiDAR system comprising:

at least one emitter configured to generate emitted radiation within a defined frequency range throughout a field of view of the scene, wherein the emitted radiation is a sequence of N successive pulses with each successive pulse having a same amplitude and duration and a different relative time shift in relation to a sampling time reference signal;

an array of detectors configured to receive reflected radiation within the defined frequency range for the field of view;

circuitry operably coupled to the array of detectors to generate and store digital information corresponding to each detector in the array of detectors, the digital information for each detector in the array of detectors being sampled and stored in a unique one of a set of frame buffers in response to the sampling time reference signal; and

a processing system operably connected to the set of frame buffers and configured to construct a sampled waveform representing the field of view based on the digital information in the set of frame buffers having an effective sampling frequency that is N times a frequency of the sampling time reference signal, and to output at least one parameter based on an analysis of the sampled waveform.

2. The LiDAR system of claim 1 wherein one of the parameters in the analysis is a distance to an object based on time of flight of the emitted and reflected radiation, or an angle of an object associated with the reflected radiation.

3. The LiDAR system of claim 1 wherein the processing system includes at least one graphics processing unit (GPU) configured to process the stored information using a volumetric analysis of information for at least a given detector as stored in at least the frame buffer corresponding to the given detector that is performed for multiple points in time or samples within that frame buffer such that multiple periodic samples are evaluated to reconstruct and analyze the reflected radiation.

4. The LiDAR system of claim 1 wherein the at least one emitter generates emitted radiation at two different frequencies with overlapping fields of view within the scene, and wherein the processing system performs frequency analysis on information for at least a given detector as stored in at least the frame buffer corresponding to the given detector to determine sampling times at which differences in frequency waveforms are detected.

5. The LiDAR system of claim 4 wherein the processing system outputs the at least one parameters based on the analysis of the differences in frequency waveforms.

6. The LiDAR system of claim 5 wherein the frequency analysis is a Fast Fourier Transform.

7. The LiDAR system of claim 1 wherein the emitted radiation is emitted in packets and the processing system includes at least one graphics processing unit (GPU) configured to produce multiple output points for each packet of the emitted radiation corresponding to a pre-defined grid of points for the field of view of the emitter.

8. The LiDAR system of claim 7 wherein the GPU is configured to utilize at least one analysis selected from a segmentation analysis to differentiate between multiple objects in a single field of view, an edge-detection process to define an edge feature describing an edge of an object in the field of view, or a corner-detection process to define a corner feature describing a corner of an object in the field of view.

9. The LiDAR system of claim 1 wherein the set of frame buffers includes at least three frame buffers corresponding to each detector that are collectively analyzed by the processing system for that detector.

10. The LiDAR system of claim 9 wherein the processing system collectively analyzes the at least three frame buffers for each detector to compare relative intensities among detectors in the detector array, or to determine a direction and a rate of a slope of objects detected within the field of view based on a time domain analysis, or both.

11. The LiDAR system of claim 1 wherein the at least one emitter is configured for in situ calibration by emitting pre-determined calibration patterns of emitted radiation and measuring at least one of an intensity, a location or an angle of the reflected radiation.

12. The LiDAR system of claim 1 wherein each detector in the detector array has a unique angle through which the reflected radiation is received.

13. The LiDAR system of claim 1 wherein the emitted radiation is collimated electromagnetic energy selected from the wavelength ranges of:

ultraviolet (UV)—

100-400 nm, visible—

400-700 nm, near infrared (NIR)—

700-1400 nm, infrared (IR)—

1400-8000 nm, long-wavelength IR (LWIR)—

8 um-15 um, or far IR (FIR)—

15 um-1000 um.

14. The LiDAR system of claim 1 wherein the emitter is a laser diode.

16. The LiDAR system of claim 1 wherein the emitter utilizes two or more frequencies that have a reflected radiation that produces a chirp frequency as a difference between the frequencies of the emitted radiation.

19. The LiDAR system of claim 10 wherein the incident radiation is a sequence of N successive pulses with each successive pulse exhibiting the same amplitude and duration and a relative time shift in relation to the sampling time reference signal, and wherein the processing system is configured to construct a version of the sampled data having an effective sampling frequency that is N times a frequency of the sampling time reference signal.

15. The LiDAR system of claim 15 wherein the laser diode is tunable by a waveform that represents a bias voltage or a bias current that varies over a sampling period defined by successive ones of the sampling time reference signal.

17. The LiDAR system of claim 17 wherein the processing system performs an N-sample Fast Fourier Transform to determine a time location of the chirp frequency for each detector.

18. A light detecting and ranging (LiDAR) system for acquiring information about objects in a scene, the LiDAR system comprising:

an array of detectors configured to receive retroreflected radiation within a defined frequency range for a field of view of the scene;

circuitry operably coupled to the array of detectors including analog-to-digital (A/D) converters to generate digital data and sampling circuitry to generate a sampling time reference signal;

a set of frame buffers operably coupled to the array of detectors and the circuitry to sample and configured to store digital data from each detector in response to the sampling time reference signal as sampled data;

a plurality of emitters to generate incident radiation within the defined frequency range throughout the field of view, the incident radiation being generated at two frequencies with overlapping fields of view within the scene, the difference between the frequencies being designated as Δ

f;

a processing system configured to perform a frequency analysis on the sampled data to determine sampling times at which Δ

f waveforms are detected and generate at least one parameter based on the frequency analysis.

20. A method for detecting and identifying an object within a field of view of a scene comprising:

providing a LiDAR system, the LiDAR system including an emitter and a detector array;

activating the emitter to emit a series of N successive pulses over a field of view for the scene, wherein each successive pulse has a same amplitude and duration and a different relative time shift in relation to a sampling time reference signal;

sampling a portion of reflected energy from the emitted light pulses utilizing the detector array at a frequency that is N times the sampling time reference signal and storing the sampled data in a set of frame buffers;

using a processing system to construct a sampled waveform representing the field of view based on the data in the set of frame buffers, and analyze sampled waveform to identify and detect an object within the field of view.