Burak Kaleci
Papers
17
Total Citations
103
H-Index
6
About
Burak Kaleci is a robotics researcher whose work spans autonomous robot navigation, multi-robot coordination, and semantic environment understanding. His research is particularly focused on equipping mobile robots with the intelligence needed to interpret and navigate complex indoor environments effectively. Kaleci has made notable contributions to multi-robot task allocation, proposing market-based auction mechanisms that leverage energy models to efficiently assign tasks across heterogeneous robot teams — work that has garnered 15 and 7 citations respectively. A significant thread of his research addresses semantic place classification, developing probabilistic, rule-based, and deep learning approaches — including the 2DLaserNet architecture — to distinguish indoor locations such as rooms, corridors, and doorways using 2D laser scan data, collectively accumulating nearly 30 citations across multiple publications. His work on spatial representation is equally impactful, with contributions to constructing topological maps from metric maps using spectral clustering (13 citations) and comparative studies of map construction methods. More recently, he has extended his expertise to 3D point cloud analysis for planar surface segmentation. Across his career, Kaleci's research consistently bridges perception, mapping, and coordination challenges in robotics, offering practical solutions that advance the autonomy of mobile robot systems in real-world indoor settings.
Research Focus
Key Achievements
Top Papers
- 1Market-based task allocation by using assignment problem15 citations · 2010
- 2Constructing Topological Map from Metric Map Using Spectral Clustering13 citations · 2015
- 3
- 4
- 5Rule-Based Door Detection Using Laser Range Data in Indoor Environments9 citations · 2015
- 6
- 7Semantic classification of mobile robot locations through 2D laser scans6 citations · 2019
- 8
- 9
- 10