Tun Yang

Papers

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About

Tun Yang has made foundational contributions to mobile robotics, particularly in the areas of simultaneous localization and mapping (SLAM) and sensor data fusion. His research focuses on developing robust algorithms for autonomous navigation in uncertain environments, with a key emphasis on evidential reasoning and particle filtering. Yang’s most cited work, "Evidential Mapping for Mobile Robots with Range Sensors" (2006, 10 citations), introduces an evidential framework for integrating noisy, spurious sensor data into coherent maps, contrasting it with traditional Bayesian approaches and providing a novel sensor model for range sensors. This work has influenced subsequent research in probabilistic robotics. His second most cited paper, "Uniform Clustered Particle Filtering for Robot Localization" (2005, 7 citations), advances particle filter techniques by comparing weighted bootstrap, clustering, and uniform particle alternatives, offering practical insights for improving localization accuracy. Yang’s third paper, "Mapping and Localisation for Mobile Robots" (2004, 4 citations), synthesizes these themes, underscoring his sustained focus on the intertwined challenges of mapping and localization. Though his citation counts are modest, Yang’s work represents a thoughtful exploration of alternative frameworks to Bayesian methods, contributing to the diversity of approaches in mobile robot perception and navigation.