Max Robotics — Global Service Robot Directory + US Certification

About

Yingyu Wang is a robotics researcher whose work bridges the critical gap between perception and mapping for autonomous systems. Her primary research areas include simultaneous localization and mapping (SLAM), 3D reconstruction, and depth estimation, with a particular focus on optimizing non-feature-based representations. Wang’s most significant contribution is the development of Occupancy-SLAM, a novel framework that jointly optimizes robot poses and continuous occupancy maps—a departure from traditional feature-based SLAM approaches. Her foundational 2022 paper on this topic (11 citations) demonstrated that integrating pose and map optimization yields more accurate and robust results, while her 2025 follow-up (4 citations) further refined the algorithm’s efficiency and robustness. In computer vision, Wang introduced a dual-cue network for multispectral photometric stereo (2019, 27 citations), advancing 3D surface reconstruction under varying lighting conditions. She also proposed a self-supervised depth completion method with an attention-based loss (2020, 3 citations), addressing the challenge of predicting dense depth from sparse inputs—a key enabler for autonomous driving and robotics. Wang’s work is distinguished by its focus on practical, optimization-driven solutions that push the boundaries of how robots understand and navigate their environments.