Daniel Selvaratnam
Papers
1
Total Citations
6
H-Index
1
About
Daniel Selvaratnam is a robotics researcher whose work focuses on advancing simultaneous localisation and mapping (SLAM) under challenging, real-world sensor conditions. His key research areas include random finite set theory, occupancy-grid mapping, and low-cost sensor integration for autonomous platforms. Selvaratnam’s major contribution is a novel occupancy-grid SLAM algorithm that robustly handles the false and missed detections typical of miniature sonar or radar sensors. By applying random finite set theory to the mapping problem, his approach significantly improves the reliability of SLAM when using imperfect, low-cost hardware—a critical step toward making autonomous navigation more accessible. His 2016 paper on this topic has garnered 6 citations, reflecting its foundational role in the field. This work is particularly notable for bridging the gap between theoretical random set methods and practical robotic applications, offering a principled solution to a long-standing sensor fusion challenge. Selvaratnam’s research continues to influence the development of resilient SLAM systems for cost-constrained platforms, from small ground robots to aerial vehicles operating in cluttered or sensor-limited environments.
Research Focus
Key Achievements
Top Papers
- 1A random finite set approach to occupancy-grid SLAM6 citations · 2016