Feng Tan

Papers

Total Citations

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About

Feng Tan is a researcher whose work addresses one of the most fundamental challenges in robotics: simultaneous localization and mapping (SLAM). His key contributions lie in developing analytical approaches to SLAM that eliminate the need for linearized approximations, a common source of error in traditional methods. In his most-cited paper, "Analytical SLAM Without Linearization" (2017), Tan applies a novel combination of linear time-varying (LTV) Kalman filtering and nonlinear contraction tools to achieve robust, provably convergent state estimation. By introducing virtual synthetic measurements, his framework avoids the inaccuracies of extended Kalman filters and particle filters, offering a more principled and mathematically rigorous solution. Although his citation counts are modest—with his top paper garnering 8 citations—his work represents a significant theoretical advance in nonlinear estimation for robotics. Tan’s research is particularly valuable for students and engineers seeking a deeper understanding of the mathematical foundations of SLAM, offering a pathway toward more reliable autonomous navigation systems.