About

Igor Spasojevic is a robotics researcher whose work centers on autonomous navigation, metric-semantic SLAM, and multi-robot coordination for size, weight, and power (SWaP) constrained aerial systems. His major contributions include developing the first real-time decentralized metric-semantic SLAM algorithm for heterogeneous robot teams (SlideSLAM, 2025), and pioneering active collaborative localization methods that enable GPS-denied multi-floor exploration using only onboard sensing. His 2024 paper on UAVs for forestry—which introduced metric-semantic mapping and autonomous diameter estimation—has already garnered 24 citations, while his work on 3D Active Metric-Semantic SLAM (21 citations) addresses the critical challenge of exploration under state uncertainty. Spasojevic has also advanced trajectory optimization for field-of-view constrained flight, joint feature selection for high-speed vision-aided navigation, and GPU-accelerated Gaussian splatting for real-time exploration (RT-GuIDE, 2025). His research consistently bridges theoretical planning and control with practical deployment on resource-limited aerial platforms, making him a rising figure in autonomous robotics and field deployment of intelligent aerial systems.

Research Focus

Key Achievements

5
H-Index
10
Papers
73
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
UAVs for forestry: Metric-semantic mapping and diameter estimation with autonomous aerial robots
24 citations · 2024
📈 Most Prolific Year: 2024 (3 Papers)
🤝 Key Collaborators: 23
🏛 Institutions: University of Pennsylvania, Massachusetts Institute of Technology, University of California, Riverside, Global and Regional Asperger Syndrome Partnership

Top Papers

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    3D Active Metric-Semantic SLAM
    21 citations · 2024
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago