Jinliang Cong
Papers
3
Total Citations
63
H-Index
3
About
Jinliang Cong is a robotics researcher whose work centers on enabling autonomous mobile robots to perceive, navigate, and interact with unknown environments. His primary contributions lie at the intersection of computer vision, robotic manipulation, and simultaneous localization and mapping (SLAM). Cong’s most impactful work, "A YOLO-GGCNN based grasping framework for mobile robots in unknown environments" (2023), has garnered 52 citations, demonstrating its significance in the field. This framework integrates advanced object detection with grasp planning, allowing mobile manipulators to autonomously identify and pick objects without prior environmental knowledge. His earlier foundational paper, "A Mobile Robotic Arm Grasping System with Autonomous Navigation and Object Detection" (2021), established a complete pipeline for indoor service robots, combining navigation and visual recognition. More recently, Cong has addressed the critical challenge of SLAM in dynamic environments, proposing a real-time RGB-D system that fuses semantic and depth information to maintain robust localization even when moving objects are present. Through these contributions, Cong is advancing the practical deployment of intelligent robots in unstructured, real-world settings, from homes to industrial floors.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3