Papers
24
Total Citations
269
H-Index
9
About
Shoaib Ehsan is a prominent researcher specializing in robotics perception, autonomous systems, and visual place recognition (VPR), with a particular focus on enabling robots to navigate and localize themselves reliably in complex, real-world environments. His influential 2018 review of sensors and SLAM (39 citations) established him as a key synthesizer of knowledge in autonomous robot navigation, while his extensive body of work on VPR has fundamentally advanced how robots recognize previously visited locations under challenging conditions of appearance change and viewpoint variation. Ehsan has pioneered innovative approaches including binary neural networks and bio-inspired fly-based voting units for memory-efficient recognition, making sophisticated perception accessible on resource-constrained hardware. His development of VPR-Bench, an open-source evaluation framework, has provided the research community with standardized tools for rigorous method comparison. Beyond ground robotics, he has critically examined VPR performance for aerial platforms and investigated vision sensor resilience in extreme environments, including nuclear inspection settings. His early contributions to robot manipulator control using sliding mode techniques further demonstrate his broad robotics expertise. With over 200 cumulative citations, Ehsan's work bridges theoretical innovation and real-world robotic deployment with consistent practical impact.
Research Focus
Key Achievements
Top Papers
- 1Sensors, SLAM and Long-term Autonomy: A Review39 citations · 2018
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- 6Sliding Mode Control of Robot Manipulators via Intelligent Approaches17 citations · 2010
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