Max Robotics — Global Service Robot Directory + US Certification

About

Hao Qi is a robotics researcher specializing in Visual Simultaneous Localization and Mapping (VSLAM) for dynamic indoor environments. His work addresses a critical challenge in mobile robotics: enabling accurate perception and navigation when traditional static-environment assumptions fail. Qi’s major contributions center on developing semantic and adaptive SLAM systems that robustly handle moving objects, occlusions, and appearance changes. His most cited work, “ATY-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments” (2023, 8 citations), introduces a semantic approach to filter dynamic features in real-time. He further advanced the field with “AGAM-SLAM: An Adaptive Dynamic Scene Semantic SLAM Method Based on GAM” (2023, 5 citations) and a novel closed-loop detection algorithm for online bag-of-words model updating (2023, 5 citations), which improves loop closure accuracy in changing indoor scenes. His most recent work (2025) optimizes feature point and keyframe selection for high-dynamic environments. With a focused publication record from 2023–2025, Qi is establishing himself as an emerging voice in robust, real-world VSLAM, pushing toward truly autonomous indoor robots.