Hua-En Chang

About

Hua-En Chang is a robotics researcher whose work focuses on advancing autonomous navigation through improved map-building techniques for unknown environments. His primary research areas include simultaneous localization and mapping (SLAM), point cloud registration, and optimization algorithms for mobile robotics. Chang’s major contribution lies in enhancing the Iterative Closest Point (ICP) algorithm—a cornerstone method for aligning 2D and 3D spatial data—by integrating Particle Swarm Optimization (PSO). In his most cited work, “Map building of unknown environment using PSO-tuned enhanced Iterative Closest Point algorithm” (2013, 6 citations), he proposed a novel approach that uses PSO to fine-tune ICP parameters, effectively filtering outliers and avoiding false matching points during map construction. His follow-up study, “Robotic map building by fusing ICP and PSO algorithms” (2014, 2 citations), further refined this fusion to overcome ICP’s tendency to get trapped in local optima, yielding more accurate transformation results. While modest in citation count, Chang’s work represents a practical step toward robust, real-time mapping for autonomous robots operating in unstructured settings—a foundational challenge in robotics that continues to inspire researchers in SLAM and sensor fusion.

Research Focus

Key Achievements

Top Papers

1. 1
   Map building of unknown environment using PSO-tuned enhanced Iterative Closest Point algorithm

   6 citations · 2013
2. 2
   Robotic map building by fusing ICP and PSO algorithms

   2 citations · 2014

Key Collaborators

Contact & Links