Yixuan Xu

About

Yixuan Xu is a researcher at the forefront of autonomous vehicle perception and mobile robotics. Their primary research areas include LiDAR-based 3D object detection, panoptic segmentation, and mobile robot path planning. Xu’s most significant contribution is the development of **AOP-Net (All-in-One Perception Network)**, a pioneering multi-task framework that unifies 3D object detection and panoptic segmentation into a single LiDAR-based system. This integrated approach addresses two critical perception tasks simultaneously, offering a more efficient and holistic solution for autonomous driving and robotic navigation. The work on AOP-Net has garnered early recognition, with its primary publication accumulating 3 citations shortly after release. Complementing this, Xu also authored a comprehensive review of mobile robot path-planning algorithms, systematically categorizing them into conventional, smart search, sampling-based, and AI-driven methods. This review provides a valuable roadmap for researchers navigating the complex landscape of autonomous navigation. With a clear focus on bridging perception and planning, Yixuan Xu is establishing a reputation for creating efficient, unified architectures that push the boundaries of what autonomous systems can achieve.

Research Focus

Key Achievements

Top Papers

1. 1
   AOP-Net: All-in-One Perception Network for LiDAR-based Joint 3D Object Detection and Panoptic Segmentation

   3 citations · 2023
2. 2
   A Review of Mobile Robot Path-planning Research

   2 citations · 2023
3. 3
   AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation

   2 citations · 2023

Key Collaborators

Contact & Links