IP Library Granted Patent US 10,989,542
Granted Patent B2
US 10,989,542 · App. 16/745,775 · Granted Apr 27, 2021

Aligning measured signal data with slam localization data and uses thereof

Inventors: Ji Zhang (Pittsburgh, PA); Kevin Joseph Dowling (Gibsonia, PA)
Assignee: Kaarta, Inc.
G01C21/206G01C21/165G01S5/0252G06T17/05H04W4/029H04W4/38H04W84/18
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Quick Facts
Patent No.
US 10,989,542
App. No.
16/745,775
Granted
Apr 27, 2021
Kind
B2
Abstract

A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Claims (32)

1. A method comprising:

obtaining a map of a 3D geometry of an environment;

receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one non-spatial attribute of a location within the environment; and

matching the plurality of received sensor readings to a representative corresponding location in the map to produce a resulting map of 3D aspects of the environment, the map comprising at least on non-spatial attribute value for each non-spatial attribute at the corresponding location within the environment,

wherein the matching comprises utilizing a plurality of lower resolution sensor readings to determine an approximate sensor location and utilizing a plurality of higher resolution sensor readings to determine a determined sensor location.

2. The method of claim 1 , wherein the received plurality of sensor readings correspond to a generally stationary position of the device.

3. The method of claim 1 , wherein the received plurality of sensor readings correspond to a movement of the device through the environment.

4. The method of claim 1 , wherein each of the sensor readings is derived from at least one of LIDAR, a camera, a depth camera, an infrared camera, an IMU, an ultrasonic sensor, a Wi-Fi sensor, a Bluetooth sensor, a temperature sensor, magnetometer, SONAR, RADAR, X-ray imaging, magnetic resonance imaging, tomography imaging, ground penetrating radar, radioactivity detector, or a barometer.

5. A system comprising:

a device comprising at least one sensor the device adapted to receive a plurality of sensor readings within an environment wherein each of the sensor readings corresponds to at least one non-spatial attribute; and

a processor adapted to obtain a map of a 3D geometry of the environment, the map comprising a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, wherein the processor is further adapted to match the plurality of received sensor readings to at least one location in the map,

wherein the matching comprises utilizing a plurality of lower resolution sensor readings to determine an approximate sensor location and utilizing a plurality of higher resolution sensor readings to determine a determined sensor location.

6. The system of claim 5 , wherein a portion of the plurality of non-spatial attribute values are determined via an interpolation of the non-spatial sensor readings.

7. The system of claim 6 , wherein the interpolation is non-linear.

8. The system of claim 5 , wherein the received plurality of sensor readings correspond to a generally stationary position of the device.

9. The system of claim 5 , wherein the received plurality of sensor readings correspond to a movement of the device through the environment.

10. The system of claim 5 , wherein each of the sensor readings is derived from at least one of LIDAR, a camera, a depth camera, an infrared camera, an IMU, an ultrasonic sensor, a Wi-Fi sensor, a Bluetooth sensor, a temperature sensor, magnetometer, SONAR, RADAR, X-ray imaging, magnetic resonance imaging, tomography imaging, ground penetrating radar, radioactivity detector, or a barometer.

11. A method comprising:

accessing a map comprising a point cloud of an environment comprising a plurality of non-spatial attribute values;

receiving, for each of a plurality of non-spatial attributes, a plurality of non-spatial sensor readings within the environment; and

referencing the received plurality of non-spatial sensor readings to the map resulting in a referenced map,

wherein the referencing comprises utilizing a plurality of lower resolution sensor readings to determine an approximate sensor location and utilizing a plurality of higher resolution sensor readings to determine a determined sensor location.

12. The method of claim 11 , wherein a portion of the plurality of non-spatial attribute values are determined via an interpolation of the non-spatial sensor readings.

13. The method of claim 12 , wherein the interpolation is non-linear.

14. The method of claim 11 , wherein the received plurality of sensor readings correspond to a generally stationary position of a device.

15. The method of claim 11 , wherein the received plurality of sensor readings correspond to a movement of a device through the environment.

16. The method of claim 11 , wherein each of the sensor readings is derived from at least one of LIDAR, a camera, a depth camera, an infrared camera, an IMU, an ultrasonic sensor, a Wi-Fi sensor, a Bluetooth sensor, a temperature sensor, magnetometer, SONAR, RADAR, X-ray imaging, magnetic resonance imaging, tomography imaging, ground penetrating radar, radioactivity detector, or a barometer.

17. The method of claim 11 , wherein the referenced map is a heat map.

18. The method of claim 17 , where the heat map is a georeferenced heat map.

19. The method of claim 1 , wherein non-spatial attributes include at least one of a radio frequency, a thermal, a visual, an acoustic, an electrical, a magnetic, or a radioactive attribute.

20. The system of claim 5 , wherein non-spatial attributes include at least one of a radio frequency, a thermal, a visual, an acoustic, an electrical, a magnetic, or a radioactive attribute.

21. The method of claim 11 , wherein non-spatial attributes include at least one of a radio frequency, a thermal, a visual, an acoustic, an electrical, a magnetic, or a radioactive attribute.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 16, 2023
From: KAARTA, INC.
To: CARNEGIE MELLON UNIVERSITY
Reel/Frame 064603/0891 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 9, 2020
From: ZHANG, JI; DOWLING, KEVIN JOSEPH
To: KAARTA, INC.
Reel/Frame 052878/0960 →
Continuity (13)
Continuation PCTUS2018042346 · Jul 16, 2018
Continuation In Part PCTUS2018040269 · Jun 29, 2018
Continuation In Part PCTUS2018015403 · Jan 26, 2018
Continuation In Part PCTUS2017055938 · Oct 10, 2017
Continuation In Part PCTUS2017021120 · Mar 7, 2017
Continuation PCTUS2017021120 · Mar 7, 2017
Continuation PCTUS2018015403 · Jan 26, 2018
Provisional Application 62527341 · Jun 30, 2017
Provisional Application 62451294 · Jan 27, 2017
Provisional Application 62406910 · Oct 11, 2016
Provisional Application 62307061 · Mar 11, 2016
Provisional Application 62533261 · Jul 17, 2017
Related Publication 20200217666A1 · Jul 9, 2020
Cited By (2)
US 12,493,120 US 12,504,523