Daniel Engelsman

I am a Ph.D. researcher at the Autonomous Navigation and Sensor Fusion Lab (ANSFL), led by Prof. Itzik Klein at the University of Haifa.

My research focuses on end-to-end Guidance, Navigation, and Control (GNC) for autonomous aerial systems, with particular emphasis on the interface between state estimation and feedback control. A central question underlying my work is:

“How does estimation quality—in terms of bias (accuracy) and noise (precision)—propagate through the GNC layers to affect closed-loop stability and tracking performance?”.

By pioneering Estimation-Aware (EA) control frameworks, I build unified GNC pipelines that explicitly account for estimation error—from high-bandwidth inner-loop stabilization to lower-frequency outer-loop path planning. Replacing classical, decoupled subsystems with a unified framework establishes principled operating bounds across system and environmental constraints, unlocking precise and resilient autonomy in GNSS-denied environments.

Open Questions I’m Exploring

• Uncertainty Propagation Across Time Scales: How does state estimation uncertainty propagate across multi-rate GNC layers? Can we establish spatiotemporal mappings that inform every level of the hierarchy without bottlenecking trajectory planners?

• Guaranteed Safety Under Active Degradation: How do we construct dynamic safety certificates when sensor availability and noise statistics degrade unannounced? Can we bound state extrapolation—covering both platform dynamics (internal) and environmental interactions (external)—during extreme dead reckoning?

• Resource-Efficient Vision-Aided Navigation: How can state-of-the-art VINS and SLAM algorithms be restructured to preserve state-space consistency and guarantee bounded uncertainty on resource-constrained flight hardware?