Daixian Zhu
Papers
8
Total Citations
64
H-Index
6
About
Daixian Zhu is a robotics researcher whose work centers on simultaneous localization and mapping (SLAM), mobile robot navigation, and computer vision, with a particular focus on challenging real-world environments such as underground coal mine tunnels. Over more than a decade of sustained research, Zhu has made meaningful contributions to improving the accuracy and robustness of SLAM algorithms, tackling persistent problems such as particle weight degradation, particle depletion, and visual feature tracking loss under poor lighting and uneven terrain conditions. His early work explored binocular vision-based SLAM using optimized SIFT feature extraction, while later research integrated visual and inertial measurement units to address the localization challenges unique to mine environments. Notably, his LSO-FastSLAM algorithm drew inspiration from lion swarm optimization to enhance rescue robot positioning accuracy. Zhu has also investigated wireless-network-assisted indoor path planning and particle filter improvements for mobile robots operating in communication-dependent settings. With a cumulative body of work attracting over 60 citations, his research offers practical, safety-oriented solutions for autonomous robots deployed in hazardous and perceptually degraded environments, making his contributions especially valuable to the fields of mining robotics and search-and-rescue automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Mobile Robot SLAM Algorithm Based on Improved Firefly Particle Filter12 citations · 2019
- 3Binocular Vision-SLAM Using Improved SIFT Algorithm11 citations · 2010
- 4
- 5A SLAM method to improve the safety performance of mine robot8 citations · 2019
- 6Indoor robot path planning assisted by wireless network6 citations · 2019
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- 8