Deepan Lobo

Papers

Total Citations

H-Index

About

Deepan Lobo is a researcher at the forefront of neuromorphic vision and robotics, specializing in event-based sensing for real-time perception. His most cited work, "Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots" (2021, 33 citations), introduces a novel approach that leverages the asynchronous, low-latency output of event cameras to detect and track multiple mobile robots in dynamic indoor environments. By combining density-based spatial clustering (DBSCAN) with a single k-dimensional tree for efficient tracking, Lobo’s method achieves robust performance even under challenging lighting conditions and rapid motion—capabilities that traditional frame-based cameras struggle to match. This contribution is pivotal for applications in autonomous navigation, swarm robotics, and human-robot interaction, where speed and accuracy are critical. Lobo’s work demonstrates a deep understanding of how to harness the unique advantages of event-driven vision, positioning him as an emerging leader in the field. His research not only advances the state of the art in real-time robotic perception but also opens new pathways for deploying neuromorphic sensors in real-world, resource-constrained systems.