Swarnendu Biswas

Papers

Total Citations

H-Index

About

Swarnendu Biswas is a researcher whose work sits at the intersection of embedded systems, approximate computing, and autonomous navigation. He is best known for advancing the practical application of state estimation techniques in computer systems, particularly through his highly cited work on Kalman filtering. His 2017 paper, “An Elementary Introduction to Kalman Filtering” (19 citations), has become a key educational resource, bridging the gap between classic signal processing theory and modern challenges like dynamic voltage and frequency scaling in processors. Biswas has also made significant contributions to the field of autonomous robotics. In his 2020 paper, “A Methodology for Principled Approximation in Visual SLAM” (6 citations), he pioneered a framework for applying approximate computing to Simultaneous Localization and Mapping (SLAM) algorithms. This work directly addresses the critical trade-off between computational efficiency and accuracy in resource-constrained systems, such as those found in drones and autonomous vehicles. By systematically reducing time and energy requirements without catastrophic failure, Biswas’s research helps enable more robust, real-time decision-making in autonomous agents navigating uncertain environments.