Mingkai Jia

About

Mingkai Jia is a robotics researcher whose work addresses one of the field’s most persistent challenges: enabling robots to reliably perceive and operate in dynamic, real-world environments. His primary research areas span dynamic awareness mapping, point cloud processing, and industrial manipulation. Jia’s most impactful contribution is the development of novel frameworks for dynamic point removal in 3D maps, a critical prerequisite for robust localization and path planning. His 2023 paper on a dynamic points removal benchmark has already garnered 32 citations, establishing a standard for evaluating such systems. Building on this, his 2024 work, “DUFOMap: Efficient Dynamic Awareness Mapping,” with 28 citations, introduces a highly efficient method for detecting and filtering dynamic elements in real-time, directly addressing a core bottleneck in autonomous navigation. Beyond mapping, Jia has also contributed to industrial robotics with a fast and robust bin-picking system for densely piled objects, demonstrating his ability to tackle practical, high-impact problems. Through his focus on creating cleaner, more reliable spatial representations, Jia is laying essential groundwork for the next generation of autonomous systems that must safely and effectively share space with a dynamic world.

Research Focus

Key Achievements

Top Papers

1. 1
   A Dynamic Points Removal Benchmark in Point Cloud Maps

   32 citations · 2023
2. 2
   DUFOMap: Efficient Dynamic Awareness Mapping

   28 citations · 2024
3. 3
   Fast and Robust Bin-picking System for Densely Piled Industrial Objects

   8 citations · 2020

Key Collaborators

Contact & Links