Image processing techniques for multi-sensor inspection of pipe interiors

1. A method, comprising:

obtaining, from a multi-sensor pipe inspection robot that traverses through the interior of a pipe, two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe;

the two or more sets of condition assessment data comprising a first data type obtained using a first sensor type and a second data type obtained using a second sensor type;

identifying, using a first image processing technique applied to the first data type obtained using the first sensor type, a pipe feature;

using the pipe feature identified to identify data of the second sensor type associated with the pipe feature;

selecting, using a processor, a feature specific image processing technique based on the data identified of the second sensor type associated with the pipe feature;

combining, using a processor, two or more image processing techniques, including the first image processing technique and the feature specific image processing technique, to adjust imaging of a pipe feature; and

forming, using the processor, an image of the interior of the pipe using the two or more image processing techniques.

2. The method of claim 1 , wherein the image of the interior of the pipe is formed using the two or more sets of condition assessment data obtained from the single pass of the pipe inspection robot through the pipe interior.

3. The method of claim 1 , wherein the first data type comprises structured laser light data and the second data type comprises Light Detection and Ranging (LIDAR) data.

4. The method of claim 3 , wherein the selecting comprises selecting an image processing technique that modifies an interpolation technique used to process one or more of the first data type and the second data type associated with the feature.

5. The method of claim 4 , wherein the interpolation technique comprises a statistical technique having a predetermined association with the feature.

6. The method of claim 5 , wherein the interpolation technique is one of a nearest neighbor technique and a spline technique.

7. The method of claim 1 , wherein the image of the interior of the pipe comprises a three-dimensional image.

8. The method of claim 4 , wherein the selected image processing technique modifies a value of a data point of the first data type using a data point value of the second data type.

9. The method of claim 8 , wherein a modification is implemented by substituting the value of the data point of the first data type with a newly calculated value.

10. The method of claim 1 , comprising transmitting the two or more sets of condition assessment data to a remote device.

11. A pipe inspection system, comprising:

a multi-sensor pipe inspection robot that traverses through the interior of a pipe and obtains two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe;

the multi-sensor pipe inspection robot comprising a first sensor type and a second sensor type;

the two or more sets of condition assessment data comprising a first data type obtained using the first sensor type and a second data type obtained using the second sensor type; and

a processor that:

obtains the two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe;

identifies, using a first image processing technique applied to the first data type obtained using the first sensor type, a pipe feature;

uses the pipe feature identified to identify data of the second sensor type associated with the pipe feature;

selects a feature specific image processing technique based on the data identified of the second sensor type associated with the pipe feature;

combines two or more image processing techniques, including the first image processing technique and the feature specific image processing technique, to adjust imaging of a pipe feature; and

forms an image of the interior of the pipe using the two or more image processing techniques.

12. The pipe inspection system of claim 11 , wherein the image of the interior of the pipe is formed using the two or more sets of condition assessment data obtained from the single pass of the pipe inspection robot through the pipe interior.

13. The pipe inspection system of claim 11 , wherein the first data type comprises structured laser light data and the second data type comprises Light Detection and Ranging (LIDAR) data.

14. The pipe inspection system of claim 13 , wherein the processor selects an image processing technique that modifies an interpolation technique used to process one or more of the first data type and the second data type associated with the feature.

15. The pipe inspection system of claim 14 , wherein the interpolation technique comprises a statistical technique having a predetermined association with the feature.

16. The pipe inspection system of claim 15 , wherein the interpolation technique is one of a nearest neighbor technique and a spline technique.

17. The pipe inspection system of claim 11 , wherein the image of the interior of the pipe comprises a three-dimensional image.

18. The pipe inspection system of claim 14 , wherein the selected image processing technique modifies a value of a data point of the first data type using a data point value of the second data type.

19. The pipe inspection system of claim 18 , wherein a modification is implemented by substituting the value of the data point of the first data type with a newly calculated value.

20. A computer program product, comprising:

a non-transitory computer readable medium storing processor executable code, the code comprising:

code that obtains, from a multi-sensor pipe inspection robot that traverses through the interior of a pipe, two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe;

the two or more sets of condition assessment data comprising a first data type obtained using a first sensor type and a second data type obtained using a second sensor type;

code that identifies, using a first image processing technique applied to the first data type obtained using the first sensor type, a pipe feature;

code that uses the pipe feature identified to identify data of the second sensor type associated with the pipe feature;

code that selects a feature specific image processing technique based on the data identified of the second sensor type associated with the pipe feature;

code that combines two or more image processing techniques, including the first image processing technique and the feature specific image processing technique, to adjust imaging of a pipe feature; and

code that forms an image of the interior of the pipe using the two or more image processing techniques.