Papers
15
Total Citations
280
H-Index
10
About
Peilin Liu is a researcher specializing in autonomous navigation, simultaneous localization and mapping (SLAM), and sensor fusion systems for robotics and autonomous vehicles. His work addresses some of the most pressing challenges in mobile autonomy, particularly achieving robust, drift-free localization in complex real-world environments. Liu's most influential contribution, "Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment" (2020, 60 citations), introduced a novel adaptive system that seamlessly integrates satellite positioning with visual-inertial navigation — a critical advancement for autonomous platforms operating in GPS-denied settings such as urban canyons or tunnels. Complementing this, his tightly coupled GNSS and Vision SLAM framework (2019, 44 citations) tackled the persistent drift and scale ambiguity problems inherent to visual navigation. Beyond localization, Liu has made notable contributions to 3D point cloud processing, including a hardware-accelerated nearest neighbor search accelerator (2023, 31 citations) and feature-based LiDAR SLAM leveraging rasterized point clouds (2021, 32 citations). His broader research portfolio spans semantic mapping, life-long SLAM, reinforcement learning-based navigation planning, and robust computer vision algorithms. With over 250 cumulative citations, Liu's work demonstrates sustained and growing influence across the robotics and autonomous systems research community.
Research Focus
Key Achievements
Top Papers
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- 3A Feature Based Laser SLAM Using Rasterized Images of 3D Point Cloud32 citations · 2021
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- 5Semantic Probabilistic Traversable Map Generation For Robot Path Planning21 citations · 2019
- 6Efficient Algorithms for Maximum Consensus Robust Fitting20 citations · 2019
- 7SLAM Based Topological Mapping and Navigation14 citations · 2020
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