Zhiqiang Dang
Papers
2
Total Citations
29
H-Index
2
About
Zhiqiang Dang is a robotics researcher whose work centers on sensor fusion, state estimation, and calibration for mobile robots. His primary research areas include visual-inertial navigation systems (VINS), odometry, and multi-sensor data fusion, with a particular focus on ground robots. Dang’s most impactful contribution is his work on tightly-coupled data fusion of VINS and odometer measurements, where he introduced a method that accounts for wheel slip estimation. This approach, published in 2018 with 24 citations, addresses the critical challenge of scale unobservability in monocular visual-inertial systems when robots move with constant acceleration, significantly improving navigation accuracy in real-world conditions. Additionally, his 2020 paper on simultaneous intrinsic and extrinsic calibration of visual-odometric sensor systems, with 5 citations, provides a streamlined framework for precisely aligning measurements from multiple sensors—a fundamental requirement for robust robot navigation. Dang’s research is notable for its practical impact on autonomous ground vehicles, offering solutions that enhance reliability in environments where wheel slip and sensor misalignment are common. His work continues to influence the development of more resilient and accurate mobile robot systems.
Research Focus
Key Achievements
Top Papers
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