Internal asset model reconstruction for inspection

1 . A system comprising:

a telescopic pole having a proximal end, a distal end, and a wire encoder, wherein the distal end is controlled to extend into an interior volume of an asset and the wire encoder is configured to collect position data characterizing a position of the distal end as the telescopic pole is extended into the asset;

a rotator coupled to the distal end of the telescopic pole;

a LIDAR sensor coupled to the rotator, wherein the rotator is configured to rotate the LIDAR sensor about a first axis and the LIDAR sensor is configured to emit pulsed light waves in a two-dimensional plane to collect point cloud data as the rotator rotates and as the distal end is extended into the asset;

an inertial measurement unit (IMU) coupled to the distal end of the telescopic pole and configured to collect inertial data at the distal end as the distal end is extended into the asset; and

a computing system including at least one data processor and a memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising

receiving the position data from the wire encoder,

receiving rotational data from the rotator,

receiving the inertial data from the IMU,

receiving the point cloud data from the LIDAR sensor,

generating a surface model of the asset based on the position data, rotational data, inertial data, and point cloud data, and

providing the surface model to a user interface display communicatively coupled to the computing system.

2 . The system of claim 1 , wherein the distal end of the telescopic pole is configured to be extended along a path within the internal volume of the asset, wherein the first axis of rotation of the rotator is parallel to the path.

3 . The system of claim 1 , further comprising a pan-tilt camera assembly coupled to the rotator and the LIDAR sensor, the pan-tilt camera assembly comprising:

one or more cameras; and

a second rotator configured to tilt the pan-tilt camera assembly and the LIDAR sensor about a second axis,

wherein the at least one processor is further configured to perform operations comprising

receiving image data from the one or more cameras, and

receiving second rotational data from the second rotator, wherein the surface model is further generated based on the image data and the second rotational data.

4 . The system of claim 3 , wherein the pan-tilt camera assembly further comprises one or more lights.

5 . The system of claim 2 , wherein the at least one processor is further configured to perform operations comprising:

transmitting a first control signal to the telescopic pole operative to extend the distal end within the asset along the path.

6 . A method comprising:

controlling a distal end of a telescopic pole to be extended through one or more positions along a path within an interior volume of an asset;

controlling a rotator, provided at the distal end of the telescopic pole, to rotate about a first axis as the distal end is extended along the path within the asset;

controlling a LIDAR sensor, coupled to the rotator, to emit pulsed light waves in a two-dimensional plane as the rotator rotates,

receiving, by at least one processor of a computing system, position data characterizing a position of the distal end as the telescopic pole from a wire encoder of the telescopic pole, rotational data from the rotator, point cloud data from the LIDAR sensor, and inertial data from an inertial measurement unit (IMU) provided at the distal end;

determining, by the at least one processor, a unified point cloud based on the position data, rotational data, point cloud data, and inertial data received;

generating, by the at least one processor, a surface model of the asset based on the unified point cloud; and

providing, by the at least one processor, the surface model to a user interface display communicatively coupled to the computing system.

7 . The method of claim 6 , wherein the one or more positions include a plurality of positions, the method further comprising:

time synchronizing, by the at least one processor, the position data, rotational data, inertial data, and point cloud data received at the plurality of positions;

generating, by the at least one processor, the unified point cloud associated with the plurality of positions, the unified point cloud generated by registering a first set of point cloud data associated with a first position with a second set of point cloud data associated with a second position.

8 . The method of claim 7 , further comprising:

filtering, by the at least one processor, the unified point cloud for density using one or more of spatial averaging, density-based decimation, random decimation, and outlier removal.

9 . The method of claim 7 , further comprising:

filtering, by the at least one processor, the unified point cloud for artifacts resulting from a single pulsed light wave contacting multiple targets within the asset; and

removing, by the at least one processor, the artifacts.

10 . The method of claim 7 , further comprising:

determining, by the at least one processor, a first orientation of the first set of point cloud data associated with the first position;

determining, by the at least one processor, a second orientation of the second set of point cloud data associated with the second position; and

merging, by the at least one processor, the second set of point cloud data with the first set of point cloud data based on the first orientation and the second orientation.

11 . The method of claim 6 , wherein a pan-tilt camera assembly is coupled to the rotator and the LIDAR sensor, the pan-tilt camera assembly including one or more cameras and a second rotator, the method further comprising:

controlling the second rotator to tilt the pan-tilt camera assembly and the LIDAR sensor to tilt about a second axis;

receiving, by at least one processor, image data from the one or more cameras and second rotational data from the second rotator, wherein the unified point cloud is further determined based on the image data and second rotational data.

12 . A system comprising:

a telescopic pole having a distal end configured to be extended into an interior volume of an asset, wherein the telescopic pole comprises a wire encoder configured to collect position data characterizing a position of the distal end as the telescopic pole is extended into the asset;

a rotator, a LIDAR sensor, and an inertial measurement unit (IMU) coupled to the distal end of the telescopic pole; and

a computing system including at least one data processor and a memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

controlling the distal end of the telescopic pole to extend through one or more positions along a path within the interior volume of the asset,

controlling the rotator to rotate the LIDAR sensor about a first axis,

controlling the LIDAR sensor to emit pulsed light waves in a two-dimensional plane to collect point cloud data,

receiving the position data from the wire encoder, rotational data from the rotator, the point cloud data from the LIDAR sensor, and inertial data from the IMU,

generating a surface model of the asset based on the position data, rotational data, point cloud data, and inertial data, and

providing the surface model to a user interface display communicatively coupled to the computing system.

13 . The system of claim 12 , further comprising a second rotator and one or more cameras coupled to the distal end, wherein the at least one processor is further configured to perform operations comprising:

controlling the second rotator to rotate the one or more cameras and the LIDAR sensor about a second axis;

controlling the one or more cameras to acquire image data within the asset; and

receiving second rotational data from the second rotator and the image data from the one or more cameras, wherein the surface model is further generated based on the image data and the second rotational data.

14 . The system of claim 13 , wherein the pan-tilt camera assembly further comprises one or more lights.

15 . The system of claim 13 , wherein the second axis of rotation of the second rotator is normal to the path that the distal end of the telescopic pole is extended along.

16 . The system of claim 12 , wherein the at least one processor is further configured to perform operations comprising:

time synchronizing the position data, rotational data, inertial data, and point cloud data received at the plurality of positions;

registering point cloud data associated with each position of the plurality of positions with point cloud data associated with adjacent positions of the plurality of positions to generate unified point cloud associated with the plurality of positions.

17 . The system of claim 16 , wherein the at least one processor is further configured to perform operations comprising:

filtering the unified point cloud for density using one or more of spatial averaging, density-based decimation, random decimation, and outlier removal.

18 . The system of claim 16 , wherein the at least one processor is further configured to perform operations comprising:

filtering the unified point cloud for artifacts resulting from a single pulsed light wave contacting multiple targets within the asset; and

removing the artifacts.

19 . The system of claim 16 , wherein the at least one processor is further configured to perform operations comprising:

determining an orientation of the point cloud data associated with each position of the plurality of positions; and

merging the point cloud data associated with each position of the plurality of positions with point cloud data associated with adjacent positions based on the orientation of the point cloud data associated with each position.

20 . The system of claim 12 , wherein the first axis of rotation of the rotator is parallel to the path that the distal end of the telescopic pole is extended along.