Daji Tian

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About

Daji Tian’s research focuses on autonomous mobile robotics, with key contributions in motion control, simultaneous localization and mapping (SLAM), and real-time computing optimization. In their early work, Tian explored fuzzy-controlled obstacle avoidance for mobile robots in transportation optimization, comparing technical performances across various commercial and industrial platforms. Their most impactful paper, "Fuzzy controlled avoidance for a mobile robot in a transportation optimisation" (2011, 3 citations), laid groundwork for intelligent navigation in dynamic environments. Building on this, Tian addressed critical performance bottlenecks in SLAM systems by proposing an acceleration method using CUDA parallel computing. Their 2019 paper, "An Acceleration Method Using CUDA based on ORB-SLAM2" (2 citations), significantly improved real-time performance and positioning accuracy by offloading ORB feature extraction and matching to GPU hardware. This work directly tackled the poor real-time performance and low accuracy that plague mobile robot localization. Though early in their career, Tian’s research demonstrates a clear trajectory from foundational robotics analysis to cutting-edge parallel computing solutions, offering practical improvements for autonomous navigation systems. Their work is particularly relevant for researchers exploring GPU-accelerated SLAM and real-time robotic control.