Utility Pole Loading Analysis via Lidar and Camera Fusion
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Solution Overview
Problem
The current methods for performing pole loading and clearance analyses for utility poles are cumbersome, time-consuming, and prone to inaccuracies due to reliance on human observation, making them inefficient and costly, especially when dealing with remote or inaccessible locations.
Innovation Solution
A system combining lidar and camera data to create an overlaid image, allowing for the identification and classification of components on utility poles, enabling precise load calculations and space assessments, and providing notifications for necessary adjustments or installations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technicians physically travel to each pole location to conduct loading and clearance analyses, then measurement precision can be obtained, but productivity is significantly reduced and loss of time increases
Solution Approach 1:
The system creates a digital copy (3D model) of the utility pole and its components using lidar scanning. This virtual replica allows technicians to perform loading analyses remotely on the digital model rather than physically visiting each pole, thereby maintaining measurement precision while dramatically improving productivity and reducing travel time
Solution Approach 2:
The patent replaces the mechanical process of physical pole inspection with an optical/electromagnetic system (lidar scanning and image processing). The lidar system captures precise spatial data and component characteristics automatically, eliminating the need for manual measurement while maintaining or improving accuracy
2Measurement precision
If technicians manually inventory and calculate loads for each pole, then measurement precision is maintained, but loss of time and use of energy increase
Solution Approach 1:
The system enables self-service by allowing the digital pole model and component data to automatically provide loading information. The processed lidar data and images contain embedded measurements of component locations, sizes, and types, which can be directly used for load calculations without requiring manual inventorying or human measurement
Solution Approach 2:
Manual calculation and measurement processes are replaced with automated image processing and data analysis algorithms. The system automatically extracts component characteristics from lidar scans and images, performs load calculations, and generates reports without human intervention, reducing time loss while maintaining precision
3Ease of operation
If technicians estimate clearance space on poles, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system creates an accurate digital replica of the pole and its components, allowing for precise measurement of available clearance space in the virtual model. This eliminates the need for physical measurement or estimation while maintaining ease of operation, as the digital model can be viewed and measured remotely
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly enhances the accuracy, speed, and cost-effectiveness of pole loading and clearance analyses, reducing human error and enabling more efficient infrastructure management by providing detailed, automated assessments of pole loads and space availability.
Implementation Method 1
obtain lidar data with a lidar system
Implementation Method 2
obtain camera data with a camera system
Data Source
AI summary
A system includes a lidar system, a camera system, and a computing device. The computing device can receive lidar data from the lidar system, receive camera data from the camera system, execute object recognition process(es) to identify, from the camera data, a utility pole and components attached to the utility pole and to create corresponding classified camera data. The computing device can align the camera data with the lidar data and can classify the lidar data to create classified lidar data. The computing device can determine characteristics of the components based on the classified camera data and the classified lidar data. Based on the characteristics, the computing system can perform a pole loading analysis, a clearance analysis, and/or a wire sag analysis.