Zhenguo Chen
Papers
1
Total Citations
3
H-Index
1
About
Dr. Zhenguo Chen is a robotics researcher whose work centers on cost-effective perception systems for autonomous mobile robots, with a particular focus on monocular vision-based navigation. His most cited paper, "Costmap Construction and Pseudo-Lidar Conversion Method of Mobile Robot Based on Monocular Camera" (2021), addresses a critical challenge in robotics: enabling obstacle avoidance without expensive LiDAR sensors. By developing a method to convert monocular RGB camera data into pseudo-LiDAR representations and constructing costmaps within the ROS framework, Chen demonstrates how low-cost hardware can achieve reliable navigation. This contribution is especially valuable for educational and research platforms where budget constraints limit sensor availability. While his citation count (3) reflects an early-career stage, the work's practical significance lies in democratizing access to autonomous navigation technology. Chen's research bridges computer vision and robotics, offering a pathway for students and researchers to implement robust obstacle avoidance using only a single camera—a solution that reduces both cost and complexity. His approach exemplifies how algorithmic innovation can compensate for hardware limitations, making him a notable contributor to accessible robotics research.
Research Focus
Key Achievements
Top Papers
- 1