Carsten Hasberg
Papers
1
Total Citations
3
H-Index
1
About
Carsten Hasberg is a researcher whose work lies at the intersection of probabilistic modeling and geometric mapping, with a particular focus on Bayesian approaches to spatial representation. His key research areas include Bayesian inference, spline-based function regression, and probabilistic mapping for autonomous systems. Hasberg’s major contribution is the development of a framework that integrates interpolating global cubic splines into a general function regression model, enabling the approximation of curved functions and multi-dimensional curves from noisy observations. By rearranging the iterative process of spline parameter calculation, he created a practical and robust method for mapping complex environments. His most-cited work, “Bayesian mapping with probabilistic cubic splines” (2010), has garnered 3 citations and laid the groundwork for probabilistic approaches to geometric modeling. While his citation count is modest, Hasberg’s contributions are notable for their theoretical rigor and practical applicability in robotics and autonomous navigation, where accurate and uncertainty-aware mapping is critical. His work continues to influence researchers seeking to combine spline theory with Bayesian statistics for real-world spatial inference.
Research Focus
Key Achievements
Top Papers
- 1Bayesian mapping with probabilistic cubic splines3 citations · 2010