M. Nicolescu
Papers
1
Total Citations
3
H-Index
1
About
M. Nicolescu is a researcher whose work sits at the intersection of computer vision, robotics, and machine learning, with a particular focus on enabling intelligent robotic systems to perceive and interact with their environments. A central contribution to the field is the development of methods for object detection and pose estimation using multimodal RGB and depth data, addressing one of the most fundamental challenges in robotic vision: allowing robots to accurately identify objects and determine their spatial orientation in real time. This capability is essential for adaptive robotic grasping, a cornerstone of practical robotic manipulation in dynamic, real-world settings. By integrating color and depth information, Nicolescu's approach enhances robustness and precision in perception pipelines, moving robots closer to reliable autonomous operation. Published in 2021, this work has already begun attracting scholarly attention, reflecting its relevance to an active and rapidly evolving research community. Nicolescu's contributions speak to a broader mission of bridging the gap between theoretical computer vision and deployable robotic systems, offering tools that have meaningful implications for automation, assistive robotics, and human-robot collaboration in complex environments.
Research Focus
Key Achievements
Top Papers
- 1
Key Collaborators
Related papers
- Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping
- Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping
- Object Detection and Pose Estimation from RGB and Depth Data for Real-Time, Adaptive Robotic Grasping
- 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
- Anchor-Based Multi-Scale Deep Grasp Pose Detector With Encoded Angle Regression
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