Zachariah Goh
Papers
2
Total Citations
54
H-Index
2
About
Zachariah Goh is a leading researcher in multi-robot systems, with a primary focus on sensor calibration and perception. His most significant contribution lies in developing probabilistic approaches to the complex **AXB = YCZ calibration problem**, a fundamental challenge in coordinating multiple robots with multiple sensors. By introducing robust, uncertainty-aware methods for simultaneously determining hand-eye and robot-robot transformations, Goh has enabled more reliable and accurate multi-robot teams for applications ranging from exploration to warehouse automation. His foundational 2018 paper on this topic has garnered **45 citations**, establishing it as a key reference in the field. Goh’s work directly addresses the practical need for precise calibration in real-world deployments, making his research highly influential among roboticists and engineers. Through his rigorous probabilistic frameworks, he has helped advance the reliability of autonomous multi-agent systems, cementing his reputation as a critical contributor to modern robotics calibration theory.
Research Focus
Key Achievements
Top Papers
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