Papers
14
Total Citations
184
H-Index
9
About
Naveed Muhammad is a versatile robotics and autonomous systems researcher whose work spans robot perception, localization, human-robot interaction, and bio-inspired sensing. He is perhaps best known for his contributions to Simultaneous Localization and Mapping (SLAM), including a widely cited 2011 technique for loop closure detection using compact, histogram-based signatures derived from 3D LiDAR point clouds — a method that has garnered 49 citations and remains influential in mobile robotics. His 2009 survey on vision-based SLAM, with 17 citations, helped establish a foundational reference for researchers entering the field. Muhammad has also pioneered the use of flow-feature sensing for underwater robot localization, publishing multiple works between 2015 and 2018 that collectively demonstrate the viability of bio-inspired sensors as alternatives to conventional sonar and vision systems. Beyond perception, his research extends into human-robot interaction, including 3D-feature-based human detection and tracking for service robots, intention estimation for autonomous systems operating near humans, and socially assistive robotics for children with Autism Spectrum Disorder. His 2020 work on elastically loaded scissor mechanisms further highlights a breadth spanning mechanical design for quadruped robots. Across more than a decade of research, Muhammad has consistently advanced practical, interdisciplinary solutions for next-generation autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1Loop closure detection using small-sized signatures from 3D LIDAR data49 citations · 2011
- 2Current state of the art of vision based SLAM17 citations · 2009
- 3Underwater map-based localization using flow features14 citations · 2016
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- 8Human detection and following by a mobile robot using 3D features11 citations · 2013
- 9
- 10Human tracking by a mobile robot using 3D features7 citations · 2013