SYSTEM AND METHOD OF MULTI-SENSOR MAPPING OF AN ENVIRONMENT

1 . A robot sized and shaped for reception in a pipe, the robot comprising:

a chassis configured for movement of the robot in the pipe;

a plurality of sensors including an inertial measurement unit (IMU), an encoder and a stereo vison camera associated with the robot, and a sensor fusion system operable to combine readings from the IMU, the encoder and the stereo vision camera to determine a position of the robot within the pipe, and wherein the sensor fusion system is operable to use machine learning in creating a digital twin of the pipe.

2 . The robot of claim 1 wherein the stereo vision camera is configured to project a ring pattern on the inside of pipe, wherein the projected ring pattern is operable to infer a profile of the inside of the pipe.

3 . The robot of claim 2 wherein the profile comprises one of pipe diameter or deformation or obstruction contours of the pipe.

4 . The robot of claim 3 wherein the stereo vision camera includes a detector module wherein the detector module is configured to compute geography of the observed pattern in both left and right stereo frames.

5 . The robot of claim 3 wherein the stereo vision includes an adaptive controller configured to dynamically adjust intensity of the projected laser ring based on feedback from imagery of the stereo vision camera.

6 . The robot of claim 1 further comprising a two-dimensional camera wherein the sensor fusion system is operable to combine three-dimensional data from the stereo vision camera and two-dimensional data from the camera to create the digital twin of the interior of the pipe.

7 . The robot of claim 1 further comprising an infrared camera configured to detect relative temperatures of the pipe and wherein in the sensor fusion system is operable to combine three-dimensional data from the stereo vision camera and the detected relative temperatures to create the digital twin of the pipe.

8 . The robot of claim 7 further comprising a two-dimensional camera wherein the sensor fusion system is operable to combine three-dimensional data from the stereo vision camera and two-dimensional data from the camera to create the digital twin of the interior of the pipe and the detected relative temperatures to create the digital twin of the pipe.

9 . The robot of claim 8 wherein the digital twin of the pipe includes an indication of a lateral intersection based on the detected relative temperatures.

10 . A robot sized and shaped for reception in a pipe, the robot comprising:

a chassis configured for movement of the robot in the pipe;

a plurality of sensors including a stereo vison camera having individual cameras associated with the robot, wherein the stereo vision cameras is configured to project a laser ring image onto the interior of the pipe and wherein the stereo vision camera has a ring detector module configured to receive inputs from each of the individual cameras to form a single image of the projected laser ring whereby the single image is used in creating a digital twin of the pipe.

11 . The robot of claim 10 wherein the ring detector module is configured to fit geometric patterns to an observed pattern in both individual cameras to derive a real-time estimation of a three-dimensional representation of surface curvature inside the pipe.

12 . The robot of claim 11 wherein the ring detection module utilizes the projected ring pattern to detect anomalies within the pipe.

13 . The robot of claim 12 wherein the projected ring pattern is adaptable to dynamically vary intensity levels based on histograms associated with the stereo vision cameras.

14 . The robot of claim 10 further comprising an inertial measurement unit (IMU) sensor and an encoder, and a fusion system operable to combine readings from the IMU, the encoder and the stereo vision camera to determine a position of the robot within the pipe, and wherein the sensor fusion system is operable to use machine learning in creating a digital twin of the pipe.

15 . The robot of claim 10 wherein the fusion system tracks the projected laser rings temporally across multiple frames to refine depth estimates.

16 . A method comprising:

collecting sensor data from a plurality of sensors associated with a robot, wherein the robot is traversing an enclosed environment and wherein one of the plurality of sensors is a stereo vision camera;

recognizing a feature within the environment;

correlating the feature with the position of the robot within the environment;

mapping the feature into a three-dimensional representation of the feature;

weighting inputs from each sensor associated with the robot;

predicting the feature and location of the feature using a trained artificial intelligence algorithm; and

building a three-dimensional digital model of the environment.

17 . The method of claim 16 wherein the stereo vision camera captures a three-dimensional image of the feature.

18 . The method of claim 17 wherein the process is repeated based on the robot's movement within the environment to create multiple layers of the digital model to create an accurate three-dimensional digital model.

19 . The method of claim 17 wherein one of the plurality of sensors is an infrared sensor and wherein a sensor fusion system combines outputs of the infrared sensor and stereo vision camera to create a three-dimensional image of the feature showing heat gradients.

20 . The method of claim 19 wherein one of the plurality of sensors is an encoder configured to determine a position of the robot within the environment and wherein the weighting of the outputs inputs is a function of the position of the robot.