INDOOR POSITIONING SYSTEM TRAINING

• US 20170090477A1
• Filed: 09/25/2015
• Published: 03/30/2017
• Est. Priority Date: 09/25/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: DiLoreto, Anthony, Akselrod, Ben Z., McDuff, Steve, Robeson, Kyle D.
• Granted Patent: US 10,077,984 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/028 using a RF signal

Abstract

In an approach for collecting data, a computer identifies a virtual map of an environment. The computer determines a location associated with one or more autonomous devices within the identified virtual map. The computer provides to a first autonomous device of the one or more autonomous devices, navigation instructions from a determined location associated with the first autonomous device to a first training point. The computer collects training data associated with the first training point through the first autonomous device.

Claims

1. A method for collecting data, the method comprising:

identifying, by one or more computer processors, a virtual map of an environment;

determining, by one or more computer processors, a location associated with one or more autonomous devices within the identified virtual map;

providing, by one or more computer processors, to a first autonomous device of the one or more autonomous devices, navigation instructions from a determined location associated with the first autonomous device to a first training point; and

collecting, by one or more computer processors, training data associated with the first training point through the first autonomous device.

2. The method of claim 1, wherein determining the location associated with the one or more autonomous devices within the received virtual map further comprises:

receiving, by one or more computer processors, data from a scan of the environment, wherein the received data includes reflections that indicate objects within the scan of the environment;

calculating, by one or more computer processors, one or more distances and directions of the first autonomous device relative to the objects within the scan of the environment based on the received reflections; and

comparing, by one or more computer processors, the calculated one or more distances and directions of the first autonomous device relative to objects with one or more distances and directions of the first autonomous device relative to objects associated with the determined location of the first autonomous device within the identified virtual map.

3. The method of claim 1, further comprising:

determining, by one or more computer processors, whether an ambiguity exists in the determined location of the first autonomous device of the one or more autonomous devices within the identified virtual map;

responsive to determining the ambiguity exists, repositioning, by one or more computer processors, the first autonomous device; and

determining, by one or more computer processors, a location associated with the repositioned first autonomous device within the identified virtual map.

4. The method of claim 1, wherein collecting training data associated with the training point further comprises:

positioning, by one or more computer processors, the first autonomous device based on one of the following;

hovering at a specified height and raising an antenna to a specified height;

receiving, by one or more computer processors, an indication that the positioned first autonomous device is in position for training the training point; and

sending, by one or more computer processors, an indication to initiate training of the training point.

5. The method of claim 4, further comprising:

sending, by one or more computer processors, a received signal strength indicator at a specified level to the first training point;

collecting, by, one or more computer processors, training data associated with the first training point based on the received signal strength indicator; and

determining, by one or more computer processors, whether the collected training data exceeds a defined data threshold.

6. The method of claim 5, further comprising:

responsive to determining the collected training data exceeds the defined data threshold, identifying, by one or more computer processors, a second training point that is not trained.

7. The method of claim 1, wherein the one or more autonomous devices communicate information between the one or more autonomous devices, and wherein the communicated information includes a location, an operational status, an assigned training point, and training information.

8. A computer program product for collecting data, the computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising;

program instructions to identify a virtual map of an environment;

determining, by one or more computer processors, a location associated with one or more autonomous devices within the identified virtual map;

program instructions to provide to a first autonomous device of the one or more autonomous devices, navigation instructions from a determined location associated with the first autonomous device to a first training point; and

program instructions to collect training data associated with the first training point through the first autonomous device.

9. The computer program product of claim 8, wherein to determine the location associated with the one or more autonomous devices within the received virtual map further comprises program instructions, stored on the one or more computer readable storage media, to:

receive data from a scan of the environment, wherein the received data includes reflections that indicate objects within the scan of the environment;

calculate one or more distances and directions of the first autonomous device relative to the objects within the scan of the environment based on the received reflections; and

compare the calculated one or more distances and directions of the first autonomous device relative to objects with one or more distances and directions of the first autonomous device relative to objects associated with the determined location of the first autonomous device within the identified virtual map.

10. The computer program product of claim 8, further comprising program instructions, stored on the one or more computer readable storage media, to:

determine whether an ambiguity exists in the determined location of the first autonomous device of the one or more autonomous devices within the identified virtual map;

responsive to determining the ambiguity exists, reposition the first autonomous device; and

determine a location associated with the repositioned first autonomous device within the identified virtual map.

11. The computer program product of claim 8, wherein to collect training data associated with the training point further comprises program instructions, stored on the one or more computer readable storage media, to:

position the first autonomous device based on one of the following;

hovering at a specified height and raising an antenna to a specified height;

receive an indication that the positioned first autonomous device is in position for training the training point; and

send an indication to initiate training of the training point.

12. The computer program product of claim 11, further comprising program instructions, stored on the one or more computer readable storage media, to:

send a received signal strength indicator at a specified level to the first training point;

collect training data associated with the first training point based on the received signal strength indicator; and

determine whether the collected training data exceeds a defined data threshold.

13. The computer program product of claim 12, further comprising program instructions, stored on the one or more computer readable storage media, to:

responsive to determining the collected training data exceeds the defined data threshold, identify a second training point that is not trained.

14. The computer program product of claim 8, wherein the one or more autonomous devices communicate information between the one or more autonomous devices, and wherein the communicated information includes a location, an operational status, an assigned training point, and training information.

15. A computer system for collecting data, the computer system comprising:

one or more computer processors, one or more computer readable storage media, and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to identify a virtual map of an environment;

determining, by one or more computer processors, a location associated with one or more autonomous devices within the identified virtual map;

program instructions to provide to a first autonomous device of the one or more autonomous devices, navigation instructions from a determined location associated with the first autonomous device to a first training point; and

program instructions to collect training data associated with the first training point through the first autonomous device.

16. The computer system of claim 15, wherein to determine the location associated with the one or more autonomous devices within the received virtual map further comprises program instructions, stored on the one or more computer readable storage media, to:

receive data from a scan of the environment, wherein the received data includes reflections that indicate objects within the scan of the environment;

calculate one or more distances and directions of the first autonomous device relative to the objects within the scan of the environment based on the received reflections; and

compare the calculated one or more distances and directions of the first autonomous device relative to objects with one or more distances and directions of the first autonomous device relative to objects associated with the determined location of the first autonomous device within the identified virtual map.

17. The computer system product of claim 15, further comprising program instructions, stored on the one or more computer readable storage media, to:

determine whether an ambiguity exists in the determined location of the first autonomous device of the one or more autonomous devices within the identified virtual map;

responsive to determining the ambiguity exists, reposition the first autonomous device; and

determine a location associated with the repositioned first autonomous device within the identified virtual map.

18. The computer system of claim 15, wherein to collect training data associated with the training point further comprises program instructions, stored on the one or more computer readable storage media, to:

position the first autonomous device based on one of the following;

hovering at a specified height and raising an antenna to a specified height;

receive an indication that the positioned first autonomous device is in position for training the training point; and

send an indication to initiate training of the training point.

19. The computer system of claim 18, further comprising program instructions, stored on the one or more computer readable storage media, to:

send a received signal strength indicator at a specified level to the first training point;

collect training data associated with the first training point based on the received signal strength indicator; and

determine whether the collected training data exceeds a defined data threshold.

20. The computer system of claim 19, further comprising program instructions, stored on the one or more computer readable storage media, to:

responsive to determining the collected training data exceeds the defined data threshold, identify a second training point that is not trained.