Xiaochun Mai
Papers
5
Total Citations
52
H-Index
4
About
Xiaochun Mai is a robotics researcher whose work bridges bio-inspired intelligence, semantic mapping, and autonomous systems in dynamic environments. Her key research areas include visual SLAM, semantic mapping, hierarchical temporal memory (HTM), and agricultural robotics. Mai’s most influential contribution is her 2020 paper on improving dense mapping for mobile robots in dynamic environments using semantic information, which has garnered 20 citations and addresses a critical challenge in real-world robot navigation. She also developed a searching space constrained registration approach for an airport trolley deployment robot (14 citations), demonstrating practical impact in automating labor-intensive logistics. Earlier, Mai explored neocortex-inspired mapping strategies based on Hierarchical Temporal Memory, publishing foundational work in 2012–2013 that proposed novel bio-inspired frameworks for robot perception and navigation. Her research extends to agricultural applications, including a density map estimation model with DropBlock regularization for clustered-fruit counting (2019). With a career spanning from theoretical bio-inspired models to applied robotics in logistics and agriculture, Mai’s work exemplifies how neural-inspired algorithms can enhance robot autonomy in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
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- 3Simple perception-action strategy based on hierarchical temporal memory10 citations · 2013
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