Max Robotics — Global Service Robot Directory + US Certification

About

Zichong Chen is a robotics researcher whose work bridges the gap between autonomous navigation and real-world deployment. His primary research areas include deep imitation learning for obstacle avoidance, visual-inertial odometry (VIO), and simultaneous localization and mapping (SLAM). Chen’s most influential contribution is his 2018 paper on “Map-based Deep Imitation Learning for Obstacle Avoidance” (41 citations), which introduced a computationally efficient algorithm enabling mobile robots to make optimal navigation decisions with limited onboard resources—a critical advance for low-cost robotic platforms. He further advanced state estimation through his work on depth-enhanced VIO using Multi-State Constraint Kalman Filtering (8 citations), improving navigation accuracy by fusing sparse depth data with camera and inertial measurements. Notably, Chen contributed to the Segway DRIVE Benchmark (8 citations), a SLAM and place recognition dataset collected from a fleet of delivery robots operating in real-world environments. This dataset directly addresses the gap between academic SLAM research and practical autonomous delivery applications. His 2021 work on fuzzy gap statistics for target recognition database completion (13 citations) demonstrates his versatility in applying machine learning to robotic perception challenges.