Qianqian Zou
Papers
2
Total Citations
5
H-Index
2
About
Qianqian Zou is an emerging researcher specializing in probabilistic mapping, uncertainty estimation, and LiDAR-based perception for autonomous systems. Their work sits at the intersection of robotics, machine learning, and scene understanding, with a particular focus on addressing a critical yet often overlooked challenge in autonomous navigation: the quality and reliability of environmental representations. In their 2023 paper, "Gaussian Process Mapping of Uncertain Building Models With GMM as Prior," Zou tackles the underexplored problem of reference map uncertainty, proposing a principled probabilistic framework that combines Gaussian processes with Gaussian Mixture Models to produce richer, more reliable maps for robot localization — a contribution that has already garnered early citations in the field. Building on this foundation, their 2025 work extends uncertainty reasoning into LiDAR scene semantic segmentation, addressing the vital problem of out-of-distribution detection, a key safety concern for real-world autonomous systems. Though still in the early stages of their research career, Zou's consistent focus on uncertainty-aware methods signals a promising trajectory in robust, trustworthy autonomous perception research.
Research Focus
Key Achievements
Top Papers
- 1Gaussian Process Mapping of Uncertain Building Models With GMM as Prior3 citations · 2023
- 2