Characterizing optically reflective features via hyper-spectral sensor

• US 10,508,986 B1
• Filed: 04/23/2018
• Issued: 12/17/2019
• Est. Priority Date: 09/27/2012
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Zhu, Jiajun, Ferguson, David I., Dolgov, Dmitri A., Dowdall, Jonathan Baldwin
• Primary Examiner(s): Ratcliffe, Luke D
• Application Number:
  US15/959,611
• Time in Patent Office: 603 Days
• CPC Class Codes: F16L 55/00 Devices or appurtenances for use in, or in connection with, pipes or pipe systems the preceding groups and groups F16L57/00, F16L59/00 take precedence; repairing or joining pipes on or under water F16L1/26; nozzles B05B; cleaning of pipes B08B9/02; arrangements of draining devices for water main or service pipe systems E03B7/08; devices for preventing bursting of water pipes by freezing E03B7/10; draining devices for hydrants E03B9/14; for domestic plumbing installations E03C1/00; steam traps for draining of liquids from enclosures containing gases or vapours F16T; arrangements for sealing leaky tubes or conduits of heat-exchangers F28F11/00
• CPC Class Codes: G01C 3/08 Use of electric radiation detectors
• CPC Class Codes: G01N 21/27 using photo-electric detection G01N21/31 takes precedence; circuits for computing concentration logarithmic circuits G06G7/24; photometric circuits in general G01J
• CPC Class Codes: G01S 17/08 for measuring distance only indirect measurement G01S17/46; active triangulation systems G01S17/48
• 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 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00

Abstract

A light detection and ranging device associated with an autonomous vehicle scans through a scanning zone while emitting light pulses and receives reflected signals corresponding to the light pulses. The reflected signals indicate a three-dimensional point map of the distribution of reflective points in the scanning zone. A hyperspectral sensor images a region of the scanning zone corresponding to a reflective feature indicated by the three-dimensional point map. The output from the hyperspectral sensor includes spectral information characterizing a spectral distribution of radiation received from the reflective feature. The spectral characteristics of the reflective feature allow for distinguishing solid objects from non-solid reflective features, and a map of solid objects is provided to inform real time navigation decisions.

Claims

1. A method comprising:

generating, using a light detection and ranging (LIDAR) device associated with an autonomous vehicle, a point cloud that indicates reflective features in an environment of the autonomous vehicle;

identifying a particular reflective feature indicated in the point cloud for analysis, the analysis using data from a sensor separate from the LIDAR device, wherein the sensor is associated with the autonomous vehicle, and wherein the sensor has a spectral range of sensitivity that includes infrared radiation;

imaging, using the sensor, at least a portion of the environment that includes the particular reflective feature indicated in the point cloud, wherein the imaging generates data indicative of infrared radiation received from the particular reflective feature;

in response to identifying the particular reflective feature indicated in the point cloud for analysis, classifying the particular reflective feature based on the data indicative of infrared radiation received from the particular reflective feature;

determining a path of the autonomous vehicle based on at least the point cloud and the classification of the particular reflective feature; and

controlling the autonomous vehicle to navigate along the path.

2. The method of claim 1, wherein classifying the particular reflective feature comprises classifying the particular reflective feature as a non-solid object.

3. The method of claim 2, wherein classifying the particular reflective feature as a non-solid object comprises determining that a spectral distribution of infrared radiation received from the particular reflective feature includes one or more spectral characteristics of a non-solid material.

4. The method of claim 3, wherein the non-solid material is carbon monoxide.

5. The method of claim 3, wherein the non-solid material is water or water vapor.

6. The method of claim 1, wherein the sensor is configured to detect infrared radiation with wavelengths between 900 nm and 1700 nm.

7. The method of claim 1, wherein identifying the particular reflective feature indicated in the point cloud for analysis comprises:

determining that the particular reflective feature indicated in the point cloud resembles a non-solid object.

8. The method of claim 7, wherein the non-solid object is an exhaust plume or a water spray pattern.

9. The method of claim 7, wherein the imaging using the sensor occurs in response to determining that the particular reflective feature indicated in the point cloud resembles a non-solid object.

10. A system comprising:

a light detection and ranging (LIDAR) device;

a sensor having a spectral range that includes infrared radiation, wherein the sensor is separate from the LIDAR device;

a controller configured to perform operations, the operations comprising;

generating, using the LIDAR device, a point cloud that indicates reflective features in an environment of an autonomous vehicle;

identifying a particular reflective feature indicated in the point cloud for analysis, the analysis using data from the sensor;

imaging, using the sensor, at least a portion of the environment that includes the particular reflective feature indicated in the point cloud, wherein the imaging generates data indicative of infrared radiation received from the particular reflective feature;

in response to identifying the particular reflective indicated in the point cloud for analysis, classifying the particular reflective feature based on the data indicative of infrared radiation received from the particular reflective feature;

determining a path of the autonomous vehicle based on at least the point cloud and the classification of the particular reflective feature; and

controlling the autonomous vehicle to navigate along the path.

11. The system of claim 10, wherein classifying the particular reflective feature comprises classifying the particular reflective feature as a non-solid object.

12. The system of claim 11, wherein classifying the particular reflective feature as a non-solid object comprises determining that a spectral distribution of infrared radiation received from the particular reflective feature includes one or more spectral characteristics of a non-solid material.

13. The system of claim 10, wherein the sensor is configured to detect infrared radiation with wavelengths between 900 nm and 1700 nm.

14. The system of claim 10, wherein identifying the particular reflective feature indicated in the point cloud for analysis comprises:

determining that the particular reflective feature indicated in the point cloud resembles a non-solid object.

15. The system of claim 14, wherein the non-solid object is an exhaust plume or a water spray pattern.

16. The system of claim 14, wherein the imaging using the sensor occurs in response to determining that the particular reflective feature indicated in the point cloud resembles a non-solid object.

17. A computer readable medium storing instructions that, when executed by one or more processors in a computing device, cause the computing device to perform operations, the operations comprising:

generating, using a light detection and ranging (LIDAR) device associated with an autonomous vehicle, a point cloud that indicates reflective features in an environment of the autonomous vehicle;

identifying a particular reflective feature indicated in the point cloud for analysis, the analysis using data from a sensor separate from the LIDAR device, wherein the sensor is associated with the autonomous vehicle, and wherein the sensor has a spectral range of sensitivity that includes infrared radiation;

imaging, using the sensor, at least a portion of the environment that includes the particular reflective feature indicated in the point cloud, wherein the imaging generates data indicative of infrared radiation received from the particular reflective feature;

in response to identifying the particular reflective feature indicated in the point cloud for analysis, classifying the particular reflective feature based on the data indicative of infrared radiation received from the particular reflective feature;

determining a path of the autonomous vehicle based on at least the point cloud and the classification of the particular reflective feature; and

controlling the autonomous vehicle to navigate along the path.

18. The computer readable medium of claim 17, wherein classifying the particular reflective feature comprises classifying the particular reflective feature as a non-solid object.

19. The computer readable medium of claim 17, wherein classifying the particular reflective feature as a non-solid object comprises determining that a spectral distribution of infrared radiation received from the particular reflective feature includes one or more spectral characteristics of a non-solid material.

20. The computer readable medium of claim 17, wherein the sensor is configured to detect infrared radiation with wavelengths between 900 nm and 1700 nm.