Papers

20

Total Citations

1,549

H-Index

13

About

Ananth Ranganathan is a prominent robotics and computer vision researcher whose work has fundamentally shaped the field of simultaneous localization and mapping (SLAM) and autonomous robot navigation. His most celebrated contribution, iSAM: Incremental Smoothing and Mapping (2008), introduced a groundbreaking approach to the SLAM problem through fast incremental matrix factorization, enabling robots to efficiently and exactly update their understanding of an environment in real time — a paper that has accumulated over 1,059 citations and remains a cornerstone reference in the field. Beyond this landmark work, Ranganathan has made significant contributions to topological mapping, developing probabilistic frameworks including Rao-Blackwellized particle filters and the Online Probabilistic Topological Mapping algorithm that allow robots to infer environmental structure from sequential measurements. His research extends into semantic mapping, where he pioneered object-based place representations to support richer human-robot interaction. Additional contributions span stereo-based 3D reconstruction for navigation, place labeling via changepoint detection, and reactive robot architectures. Across more than a decade of influential research, Ranganathan has consistently advanced robots' ability to perceive, represent, and reason about their environments with remarkable mathematical rigor.

Research Focus

Key Achievements

13
H-Index
20
Papers
1,549
Total Citations
77
Avg Citations/Paper
🏆 Most Cited Paper
iSAM: Incremental Smoothing and Mapping
1,059 citations · 2008
📈 Most Prolific Year: 2008 (5 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: Honda (United States), Georgia Institute of Technology, Honda (Japan)

Top Papers

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    Loopy SAM
    32 citations · 2007
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago