Alexander Cunningham
Georgia Institute of Technology, University of Michigan–Ann Arbor
Papers
5
Total Citations
442
H-Index
5
About
Alexander Cunningham is a leading researcher in multi-robot perception and distributed simultaneous localization and mapping (SLAM), with a focus on enabling teams of autonomous robots to collaboratively explore and map unknown, often hazardous environments. His most significant contributions center on the development of the DDF-SAM (Decentralized Data Fusion - Smoothing and Mapping) framework, which provides a mathematically consistent and scalable method for multiple robots to share and fuse map information without centralized oversight. His seminal 2013 paper, "DDF-SAM 2.0," which has garnered 170 citations, introduced a robust approach to consistent distributed smoothing and mapping, allowing robots to maintain accurate global maps using only local and neighborhood data. Earlier foundational work, "DDF-SAM: Fully distributed SLAM using Constrained Factor Graphs" (157 citations), established the core algorithm for efficient, scalable map distribution. Cunningham’s research has been validated through end-to-end multi-robot mapping systems and joint experiments, demonstrating practical applications in indoor exploration and dynamic obstacle tracking for autonomous vehicles. His work is essential reading for researchers in field robotics, sensor fusion, and decentralized autonomy.
Research Focus
Key Achievements
Top Papers
- 1DDF-SAM 2.0: Consistent distributed smoothing and mapping170 citations · 2013
- 2DDF-SAM: Fully distributed SLAM using Constrained Factor Graphs157 citations · 2010
- 3
- 4Continuous-time estimation for dynamic obstacle tracking14 citations · 2015
- 5Distributed autonomous mapping of indoor environments7 citations · 2011