Point Cloud Cable Analysis for Deformed Slack Estimation

The cable is divided into multiple regions along its length, with each region represented by a separate quadratic curve model. This segmentation allows the system to capture local deformations caused by external loads (such as service lines or intermediate branches) while maintaining the simplicity of quadratic models for each segment. The division points are automatically detected based on deviations from the expected cable shape, enabling accurate slack level and tension estimation without requiring a single complex model for the entire cable.

The patent replaces manual field measurement operations with an automated point cloud analysis system. The system uses three-dimensional coordinate information from mobile mapping systems to automatically detect cable positions, fit quadratic curve models, and calculate slack levels and tensions. This substitution eliminates the need for maintenance workers to physically measure cables in the field, dramatically improving productivity while maintaining measurement accuracy through automated computational methods.

The system performs self-service by automatically processing point cloud data to extract cable information, fit models, and calculate parameters without requiring manual field measurements. The automated pipeline includes point cloud acquisition, cable detection, quadratic curve fitting, and slack level calculation, all executed without human intervention in the field. This eliminates the operational burden on maintenance personnel while preserving measurement accuracy through systematic computational analysis.