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About

Peter Zhi Xuan Li is a robotics and computer engineering researcher whose work sits at the intersection of energy-efficient computing, probabilistic mapping, and autonomous exploration. His research addresses one of the most pressing challenges in modern robotics: enabling energy-constrained robots to build and reason about 3D environmental maps without excessive memory or computational overhead. Li's most significant contribution is GMMap, a memory-efficient continuous occupancy mapping framework that leverages Gaussian Mixture Models to dramatically reduce both map size and the energy cost of memory accesses during map construction — a problem largely overlooked by prior mapping approaches. This work, already garnering 12 citations since its 2024 publication, builds naturally from his earlier real-time Gaussian fitting techniques for depth images. Alongside his mapping research, Li has made notable strides in accelerating mutual information computation for robotic exploration tasks, developing both map-scale continuous algorithms and high-throughput on-chip implementations that support efficient autonomous planning in domains such as search and rescue and space exploration. Collectively, his publications reflect a coherent research vision: bringing principled probabilistic methods to hardware-constrained robotic systems, making intelligent autonomy genuinely practical in the field.