3D track assessment method

1. A method of determining pad thickness, rail seat abrasion, and insulator thickness along a railway track bed using a system for assessing a railway track bed, the method comprising the steps of:

a. inputting elevation data, rail base edge feature coordinates, detected tie bounding box data, fastener type and location data, and rail type data to a processor wherein significant elevations due to the rail heads for each rail have been removed from the elevation data;

b. identifying all fasteners in a tie bounding box using the processor;

c. extracting tie top, rail base, and insulator measurement points for each fastener in the tie bounding box using the processor;

d. calculating the difference between a rail base elevation and a tie top elevation and adjusting for a rail base thickness to provide a rail seat abrasion measurement using the processor;

e. calculating the difference in elevation between the top of an insulator and the top of the tie to provide an insulator thickness measurement using the processor;

f. flagging the appropriate rail within the tie bounding box if a rail seat abrasion measurement is less than a defined threshold using the processor; and

g. flagging the appropriate rail within the tie bounding box if an insulator thickness measurement is less than a defined threshold using the processor.

2. The method of claim 1 further comprising the steps of determining whether the number of fasteners in the tie bounding box is less than four and, if the number is less than four, recording a broken or missing fastener for that tie bounding box using the processor.

3. The method of claim 1 further comprising the step of calculating neighborhood elevations for fastener measurement points using the processor.

4. A method of detecting joint bars and rail joints along a railway track bed using a system for assessing a railway track bed, the method comprising the steps of:

a. inputting elevation data, longitudinal and transverse elevation map sample resolution data, rail head edge coordinates, rail base edge coordinates, and joint bar models to a processor;

b. defining a 3D joint bar search zone;

c. detecting joint bar targets as objects in the defined 3D joint bar search zone for the field and gage sides of each rail using the processor;

d. determining physical parameters for joint bar targets using the processor;

e. analyzing each joint bar target to determine whether it matches any known joint bar models using the processor; and

f. determining the type of joint bar if a joint bar target matches a known joint bar model using the processor.

5. The method of claim 4 further comprising the step of eliminating joint bar targets that are not within the maximum and minimum joint bar model dimensions using the processor.

6. The method of claim 4 further comprising the step of determining the proximity of each joint bar to ties in the elevation data using the processor.

7. The method of claim 4 further comprising the step of analyzing detected joint bar targets to determine the joint bar bolt configuration using the processor.

8. The method of claim 4 further comprising the step of analyzing rail head surfaces over a defined rail head joint segment surface using the processor.

9. The method of claim 8 further comprising the step of identifying joint separation, corrugation, and battered joints using the processor.

10. A method of detecting wooden crosstie plates along a railway track bed using a system for assessing a railway track bed, the method comprising the steps of:

a. inputting elevation data, longitudinal and transverse elevation map sample resolution data, rail base edge feature coordinates, detected tie bounding box data, a 3D feature library containing plate models, 3D hole target maps, and 3D spike target maps to a processor wherein significant elevations due to the rail heads for each rail have been removed from the elevation data;

b. identifying a rail fastener search zone using the processor;

c. extracting fastener target locations from spike and hole target maps using the processor;

d. matching plate models from the 3D feature library containing plate models against the extracted fastener target locations using the processor;

e. calculating correlation strengths for matches resulting from the matching step d using the processor; and

f. retaining the plate model with the highest calculated correlation strength using the processor.