Semantic Segmentation Of 3D Point Cloud Data Acquired From Robot Dog For Scaffold Monitoring

Abstract

Many Of The Fatalities And Injuries In The Construction Industry Occur In Scaffolding Accidents, And Monitoring The Scaffolding Process And Checking Compliance Are Critical. However, Monitoring Scaffolds Is Labor-Intensive And Inefficient Because It Is Done Manually. To Address This Issue, We Propose An Advanced 3D Reconstruction Method For Detecting And Monitoring Scaffolds. Deep Learning-Based RandLA-Net Architecture Is Used To Perform Scene Segmentation. RandLA-Net Is Trained Based On Transfer Learning, Using The Knowledge Of The Model Learned With The Semantic3D Dataset. RandLA-Net Uses 3D Point Cloud Data That Are Matched And Registered By LIO-SAM, A Laser Slam Algorithm. By Attaching A LiDAR To A Quadruped Robot, It Is Possible To Obtain Data Frequently In A Manner Suitable For Construction Sites. The Proposed Methodology Has Demonstrated Good Performance In Monitoring Scaffolds.