Deepayan Chakrabarti
Papers
5
Total Citations
320
H-Index
4
About
Deepayan Chakrabarti is a leading figure in mobile robotics and 3D environmental mapping, whose work has fundamentally advanced how robots perceive and model indoor spaces. His research centers on developing efficient, real-time algorithms for autonomous mapping, with a particular focus on the expectation-maximization (EM) framework. Chakrabarti’s major contributions include pioneering the use of EM to fit low-complexity planar models to sensor data from range finders and panoramic cameras, enabling robots to generate compact, accurate 3D models of indoor environments. His seminal 2001 paper, “Using EM to Learn 3D Models of Indoor Environments with Mobile Robots,” has garnered 146 citations, establishing a foundational approach for the field. He further extended this work with a real-time EM algorithm in 2004, cited 138 times, which integrated pose estimation during mapping to produce multiplanar maps on the fly. These innovations have had lasting impact on robotics, computer vision, and autonomous navigation, providing a scalable solution for robots to understand complex indoor layouts. Chakrabarti’s research remains a cornerstone for students and engineers developing next-generation mapping systems.
Research Focus
Key Achievements
Top Papers
- 1Using EM to Learn 3D Models of Indoor Environments with Mobile Robots146 citations · 2001
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- 5Using EM to Learn 3D Environment Models with Mobile Robots4 citations · 2007