Papers
8
Total Citations
267
H-Index
5
About
Carlos Nieto is a robotics researcher whose work spans multi-robot systems, simultaneous localization and mapping (SLAM), and distributed estimation — areas central to enabling autonomous robots to operate intelligently in complex, real-world environments. His most influential contribution, "Distributed Mapping with Privacy and Communication Constraints" (2017), has garnered 135 citations and addresses a fundamental challenge in collaborative robotics: how teams of robots can build accurate maps together without reliable communication infrastructure, all while preserving data privacy. This work, complemented by his 2016 paper on distributed trajectory estimation (49 citations), established him as a key voice in privacy-aware, decentralized multi-robot coordination. Nieto has also advanced object-based SLAM frameworks and adaptive informative sampling strategies for robotic teams in time-sensitive scenarios such as search and rescue (29 citations). His earlier work on incorporating domain knowledge into SLAM through virtual measurements demonstrates a creative approach to leveraging structured environmental priors for improved localization. More recently, his research has expanded into behavioral path planning under uncertainty. Collectively, Nieto's contributions reflect a consistent commitment to making multi-robot systems more scalable, practical, and deployable in real-world conditions.
Research Focus
Key Achievements
Top Papers
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- 4Multi Robot Object-Based SLAM26 citations · 2017
- 5Applying domain knowledge to SLAM using virtual measurements20 citations · 2010
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