SYSTEM AND METHOD FOR LOCALIZING A TRACKEE AT A LOCATION AND MAPPING THE LOCATION USING SIGNAL-BASED FEATURES

• US 20150285638A1
• Filed: 04/25/2014
• Published: 10/08/2015
• Est. Priority Date: 06/12/2012
• Status: Active Grant

• Current Assignee: TRX Systems, Inc.
• Original Assignee: TRX Systems, Inc.
• Inventors: Funk, Benjamin E., Napora, Jared, Kordari, Kamiar, Verma, Ruchika, Bandyopadhyay, Amrit, Teolis, Carole
• Granted Patent: US 9,664,521 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/1652 with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G01C 21/1656 with passive imaging devices, e.g. cameras
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 21/3844 Data obtained from position sensors only, e.g. from inertial navigation
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01S 19/13 Receivers
• CPC Class Codes: G01S 19/39 the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
• CPC Class Codes: G01S 5/012 Identifying whether indoors or outdoors
• CPC Class Codes: G01S 5/014 Identifying transitions between environments
• CPC Class Codes: G01S 5/015 between indoor and outdoor environments

Abstract

A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle. The system may include an integrated self-tracking unit that can localize and self-correct such localizations.

Claims

1. A computer-implemented method of localizing a trackee at a location and generating or updating a map of the location based on sensor information, the method being implemented on a computer system that includes one or more physical processors programmed by one or more modules, the method comprising:

identifying, by a feature detection module, at least one sensor feature based on sensor information that includes a plurality of measurements that measure a motion of the trackee at the location, the at least one sensor feature being identified based on a first subset of the plurality of measurements;

identifying, by the feature detection module, a landmark using only the at least one sensor feature;

determining, by a localization and mapping module, a location estimate that estimates the location of the trackee based on at least a second subset of the plurality of measurements that is the same as or different from the first subset of the plurality of measurements, wherein a landmark position of the landmark is determined based on the location estimate; and

generating or updating, by the localization and mapping module, a map including the location based on the landmark.

2. The computer-implemented method of claim 1, wherein the at least one sensor feature includes measurements of one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

3. The computer-implemented method of claim 1, wherein the at least one sensor feature includes vector representations of one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

4. The computer-implemented method of claim 1, wherein the at least one sensor feature includes measurements of magnetic data, and wherein the landmark is identified by a feature descriptor detailing unique aspects of the landmark based on high sample rate data.

5. The computer-implemented method of claim 1, wherein the at least one sensor feature is based on magnetic data, and wherein the magnetic data includes an extrema or a sharp transition in magnetic magnitude.

6. The computer-implemented method of claim 1, wherein the at least one sensor feature is based on magnetic data, and wherein the magnetic data includes a minimum magnitude and a maximum magnitude.

7. The computer-implemented method of claim 1, wherein the landmark is not identified until the at least one sensor feature has been identified at the landmark position multiple times.

8. The computer-implemented method of claim 1, wherein the identification of the at least one sensor feature is further based on second sensor information.

9. The computer-implemented method of claim 8, wherein the second sensor information includes one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

10. The computer-implemented method of claim 8, wherein the second sensor information includes one or more of magnetic data, inertial data, global positioning data, and light data.

11. The computer-implemented method of claim 1, wherein identifying the landmark includes correlating the at least one sensor feature to a structural feature.

12. A computer-implemented method being implemented on a computer system that includes one or more physical processors programmed by one or more modules, the method comprising:

identifying, by a first module, a sensor feature based on first sensor information that includes a plurality of measurements representing a motion of a trackee at a location, the sensor feature being identified based on a first subset of the plurality of measurements;

identifying, by the first module, a landmark based on a correlation using only the sensor feature;

determining, by a second module, a location estimate that estimates the location of the trackee based on at least a second subset of the plurality of measurements that is the same as or different from the first subset of the plurality of measurements, wherein a landmark position of the landmark is determined based on the location estimate; and

generating or updating, by the second module, a map including the location based on the landmark.

13. The computer-implemented method of claim 12, wherein the plurality of measurements includes one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

14. The computer-implemented method of claim 12, wherein the sensor feature includes vector representations of one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

15. The computer-implemented method of claim 12, wherein the first subset of the plurality of measurements includes magnetic data, and wherein the landmark is identified by a feature descriptor detailing unique aspects of the landmark based on high sample rate data.

16. The computer-implemented method of claim 12, wherein the first subset of the plurality of measurements includes magnetic data, and wherein the magnetic data includes an extrema or a sharp transition in magnetic magnitude.

17. The computer-implemented method of claim 12, wherein the first subset of the plurality of measurements includes magnetic data, and wherein the magnetic data includes a minimum magnitude and a maximum magnitude.

18. The computer-implemented method of claim 12, wherein the identification of the sensor feature is further based on second sensor information.

19. The computer-implemented method of claim 18, wherein the second sensor information includes one or more of magnetic data, radiation data, inertial data, optical data, and signal strength data.

20. The computer-implemented method of claim 18, wherein the second sensor information includes one or more of magnetic data, inertial data, global positioning data, and light data.