Sayantan Datta

Indian Institute of Technology Hyderabad

Papers

Total Citations

H-Index

About

Sayantan Datta is a researcher whose work lies at the intersection of robotics, perception, and geometric data analysis, with a particular focus on advancing the robustness of Simultaneous Localization and Mapping (SLAM) systems. His most notable contribution, the paper "SLAM pose-graph robustification via multi-scale Heat-Kernel analysis" (2016), introduces a novel approach to improving the reliability of pose-graph SLAM by leveraging multi-scale heat-kernel techniques. This work addresses a critical challenge in robotics: ensuring that SLAM algorithms remain accurate even when faced with noisy or outlier-ridden sensor data. By modeling the SLAM problem as a dyadic graph of relative pose measurements, Datta’s method enhances the graph’s resilience, making it more robust in real-world environments. Although his citation count is modest (2 citations for this paper), the work demonstrates a sophisticated understanding of graph theory and spectral analysis applied to robotics. Datta’s research is particularly relevant for students and engineers working on autonomous navigation, as it offers a mathematically rigorous framework for improving SLAM performance in challenging conditions. His contributions highlight the importance of multi-scale analysis in solving complex robotic perception problems.