Papers
7
Total Citations
24
H-Index
3
About
Yibo Cao is a robotics researcher specializing in mobile robot navigation, Simultaneous Localization and Mapping (SLAM), and autonomous path planning. His work addresses fundamental challenges in enabling robots to accurately perceive, map, and navigate complex indoor environments using a range of sensing modalities. Cao's most significant contributions center on advancing SLAM algorithms and path tracking for indoor mobile robots. His PP-ST path tracking algorithm (2023, 9 citations) tackled critical limitations of the widely used Pure Pursuit approach, improving trajectory accuracy at corners and under varying forward-looking distances. His indoor SLAM work built on PL-ICP optimization, and he later extended this to multi-sensor deep fusion frameworks utilizing TSDF map models, addressing noise sensitivity and incomplete sensor utilization in traditional 2D SLAM systems. His earlier FusionMapping research (2019) explored cost-effective depth prediction by combining monocular imagery with 2D LiDAR, demonstrating an interdisciplinary approach bridging computer vision and robotics. More recently, Cao has tackled practical deployment challenges, including robust point cloud filtering to handle problematic indoor surfaces like glass and smooth floors. With a growing publication record and citations accumulating across multiple venues, his research offers meaningful advancements toward reliable, commercially viable indoor robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1PP-ST: An Indoor Mobile Robot Path Tracking Algorithm9 citations · 2023
- 2Indoor SLAM Algorithm Based on PL-ICP and Map Matching5 citations · 2021
- 3A Multi-Sensor Deep Fusion SLAM Algorithm Based on TSDF Map4 citations · 2024
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