Yangquan Guo

Papers

Total Citations

182

H-Index

About

Yangquan Guo is a leading researcher in the field of robotic autonomy, with a primary focus on Simultaneous Localization and Mapping (SLAM) for service robots. His work addresses a critical challenge: enabling robots to operate reliably in dynamic, everyday environments over extended periods—a problem known as lifelong SLAM. Guo’s most significant contribution is the creation of the OpenLORIS-Scene Datasets, a benchmark designed to rigorously test SLAM algorithms under real-world, changing conditions. The 2020 version of this dataset has garnered 163 citations, underscoring its importance as a standard evaluation tool in the robotics community. By providing diverse, long-term data sequences that capture lighting shifts, object movements, and structural changes, Guo’s datasets have pushed the field beyond static, controlled experiments. His work has been instrumental in answering the question, “Are we ready for service robots?” by highlighting both progress and remaining gaps. Through these efforts, Yangquan Guo has helped lay the groundwork for truly autonomous service robots that can navigate homes, offices, and public spaces without human intervention.