Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor

Advancements in automated 3D scene reconstruction are essential for accurately capturing and documenting the current state of buildings and infrastructure. Traditional 3D reconstruction relies on laser scanning to obtain as-built conditions, but this process is often labor-intensive and time-consuming. This study introduces an optimization algorithm incorporating methods for viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. Additionally, the research investigates 3D reconstruction methods, comparing coupled and decoupled approaches to identify the most practical configuration for robotic scanning. Automation strategies for collision avoidance in human-centric environments are also explored, with adaptive control methods tested and validated for efficient point cloud data capture in indoor environments. This research advances the state-of-the-art in robotic scanning by providing a more precise and adaptive framework for 3D scene reconstruction. The results demonstrate the effectiveness of the proposed method in achieving high scan completeness and sufficient density in point cloud data, offering solutions for efficient robotic scanning.