Lidar Mapping and Surveillance of Nuclear Infrastructure
- US-DOE (United States)
- Applied Research Center, FIU (United States)
Wear is unavoidable, whether it occurs in a nuclear facility or abridge on the highway its detection is crucial to preventing the failure of a system. While tools capable of detecting localized damage exist, industry lacks methods to evaluate the health of larger infrastructure. Routine inspection of nuclear facilities must be undertaken to ensure the safety of personnel and proper containment of nuclear materials. Most systems in a nuclear facility are designated as radioactive and confined spaces. To constantly monitor such systems in person would be hazardous but with a remote inspection tool it can be done in a safer and easier way. Lidar: Operates by emitting a laser pulse and looking for the reflected light. Allows user to gain 3D information of the environment. Project Objective: To develop an inspection tool for evaluating the health of large scale infrastructure using Lidar and machine learning. Autonomous Platform + Lidar + Data Fusion + Big Data. Use SLAM to build a 2D map of the environment. Point cloud created by transforming the points between reference frames. Point cloud to Surface: Inherent representation of surface geometry; Difficult to process with conventional techniques. Point cloud to Image: Easily processed by conventional machine learning algorithms. The quantity of data generated will necessitate a big data framework to manage the data for processing. Additional Sensors: Detecting the temperature (a) chemical makeup (b) and radiation emission (c) of a surface could assist in detecting problems. Human inspection can detect irregularities in both physical objects and their point-cloud representations. This is a good indication that mapping nuclear sites using Lidar will provide qualitative insight into the health of the facility. If deep learning can be used to automatically identify problem areas then the completed tool could become an invaluable asset.
- Research Organization:
- WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States)
- OSTI ID:
- 23005505
- Report Number(s):
- INIS-US--21-WM-P28
- Country of Publication:
- United States
- Language:
- English
Similar Records
Semi-automatic image annotation using 3D LiDAR projections and depth camera data
Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part I - Environment Mapping