Real-time system for multi-modal 3D geospatial mapping, object recognition, scene annotation and analytics

Patent metadata

Field	Value
Publication number	US-9476730-B2
Application number	US-201414575495-A
Country	US
Kind code	B2
Filing date	Dec 18, 2014
Priority date	Mar 18, 2014
Publication date	Oct 25, 2016
Grant date	Oct 25, 2016

Abstract

A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics “plug ins” to provide real-time annotation of the visual representation with domain-specific markups.

First claim

The invention claimed is:

1. A mobile computing device, comprising:
one or more processors, and, in communication with the one or more processors:
one or more image sensors configured to obtain two-dimensional image data and three-dimensional image data;
one or more non-transitory machine accessible storage media comprising instructions to cause the mobile computing device to:
temporally and spatially align the two-dimensional image data and three-dimensional image data;
generate a map representation of a geo-spatial area of the real world surroundings of the mobile computing device based on the temporally and spatially aligned two-dimensional and three-dimensional image data; and
recognize a plurality of visual features in the map representation, using one or more computer vision algorithms to:
recognize larger-scale objects;
recognize smaller-scale objects by iteratively performing context-free object identification and contextual object identification; and
recognize a complex object comprising a plurality of the smaller-scale objects, using a classifier.

2. The mobile computing device of claim 1 , comprising instructions to cause the mobile computing device to detect the larger-scale objects by determining a contextual frame of reference, and use the contextual frame of reference to identify the larger-scale objects.

3. The mobile computing device of claim 1 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant three-dimensional feature detection algorithm directly on point cloud data obtained from at least one of the image sensors.

4. The mobile computing device of claim 3 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant two-dimensional feature detection algorithm.

5. The mobile computing device of claim 1 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant two-dimensional feature detection algorithm.

6. The mobile computing device of claim 2 , comprising instructions to cause the mobile computing device to recognize the smaller-scale objects by executing a context-free feature-sharing algorithm.

7. The mobile computing device of claim 6 , comprising instructions to cause the mobile computing device to recognize the smaller-scale objects by obtaining context information and classifying the smaller-scale objects based on the context information.

8. The mobile computing device of claim 6 , comprising instructions to cause the mobile computing device to recognize the complex objects by executing a contextual bag of objects algorithm.

9. An object/scene recognition system comprising instructions embodied in one or more non-transitory computer readable storage media to and executable by one or more processors to cause a mobile computing device to:
obtain two-dimensional image data and three-dimensional image data from one or more image sensors;
temporally and spatially align the two-dimensional image data and three-dimensional image data;
recognize a plurality of visual features in the image data, using one or more computer vision algorithms to:
recognize larger-scale objects;
recognize smaller-scale objects by iteratively performing context-free object identification and contextual object identification; and
recognize a complex object comprising a plurality of the recognized smaller-scale objects, using a classifier.

10. The system of claim 9 , comprising instructions to cause the mobile computing device to detect the larger-scale objects by determining a contextual frame of reference and use the contextual frame of reference to identify the larger-scale objects.

11. The system of claim 9 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant three-dimensional feature detection algorithm directly on point cloud data obtained from at least one of the image sensors.

12. The system of claim 11 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant two-dimensional feature detection algorithm.

13. The system of claim 9 , comprising instructions to cause the mobile computing device to recognize the larger-scale objects by executing an invariant two-dimensional feature detection algorithm.

14. The system of claim 10 , comprising instructions to cause the mobile computing device to recognize the smaller-scale objects by executing a context-free feature-sharing algorithm.

15. The mobile computing device of claim 14 , comprising instructions to cause the mobile computing device to recognize the smaller-scale objects by obtaining context information and classifying the smaller-scale objects based on the context information.

16. The mobile computing device of claim 14 , comprising instructions to cause the mobile computing device to recognize the complex objects by executing a contextual bag of objects algorithm.

17. An object/scene recognition method comprising, with one or more mobile computing devices:
obtaining two-dimensional image data and three-dimensional image data from one or more image sensors;
temporally and spatially aligning the two-dimensional image data and the three-dimensional image data; and
recognizing a plurality of visual features in the image data by:
recognizing larger-scale objects;
recognizing smaller-scale objects by iteratively performing context-free object identification and contextual object identification; and
recognizing a complex object comprising a plurality of the smaller-scale objects using a classifier.

18. The method of claim 17 , comprising determining a contextual frame of reference and using the contextual frame of reference to identify the larger-scale objects.

19. The method of claim 17 , comprising executing an invariant three-dimensional feature detection algorithm directly on point cloud data obtained from at least one of the image sensors.

20. The method of claim 18 , comprising, iteratively: executing a context-free feature-sharing algorithm to recognize the smaller-scale objects, obtaining context information, and classifying the smaller-scale objects based on the context information.

Assignees

• Stanford Res Inst Int

Inventors

• Samarasekera Supun
• Hadsell Raia
• Kumar Rakesh
• Sawhney Harpreet S
• Matei Bogdan C
• Villamil Ryan

Classifications

• G01C11/02 Primary

  Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures · CPC title

• G06V10/811

  the classifiers operating on different input data, e.g. multi-modal recognition · CPC title

• G06F18/256

  of results relating to different input data, e.g. multimodal recognition · CPC title

• G06F18/24

  Classification techniques · CPC title

• G08G5/70

  Arrangements for monitoring traffic-related situations or conditions · CPC title