Papers
4
Total Citations
81
H-Index
4
About
Changhao Song is a leading researcher at the intersection of robotics, Building Information Modeling (BIM), and autonomous inspection. His work focuses on developing intelligent systems that leverage BIM as a semantic and geometric foundation to enhance the autonomy, reliability, and efficiency of robots in complex built environments. Song’s major contributions include pioneering methods for coverage path planning that optimize robotic configurations using BIM data, achieving 42 citations for his foundational 2023 paper. He has also advanced automated reality capture for indoor inspection by integrating multi-sensor quadruped robots with BIM, a work that has garnered 18 citations. In the domain of indoor localization, Song introduced a novel approach combining deep object detection with a BIM-supported object landmark dictionary for pose graph relocalization, addressing the critical challenge of semantic-aware navigation in construction settings (13 citations). His earlier work on BIM-aided scanning path planning for autonomous surveillance UAVs with LiDAR (8 citations) laid the groundwork for high-accuracy point cloud collection in cluttered indoor environments. Through these innovations, Song is shaping the future of autonomous construction robotics, enabling safer, more precise, and semantically informed building inspection and monitoring.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4