Khairuldanial Ismail
Papers
5
Total Citations
34
H-Index
4
About
Khairuldanial Ismail is a robotics researcher whose work focuses on solving one of the field’s most persistent challenges: reliable localization and navigation for autonomous robots in GPS-denied and geometrically-degraded environments. His key research areas span sensor fusion, simultaneous localization and mapping (SLAM), and the innovative use of existing communications infrastructure for positioning. Ismail’s major contributions include pioneering the use of low-cost LTE signals for mobile robot localization in urban canyons, developing collaborative radio SLAM for multi-robot systems using WiFi fingerprint similarity, and creating a WiFi similarity-based odometry method that extends localization beyond wheeled platforms. His work on efficient WiFi-LiDAR SLAM addresses the critical limitations of traditional LiDAR systems in large, feature-poor environments by leveraging ubiquitous WiFi infrastructure for loop closure detection. With multiple papers each garnering 8 citations, Ismail’s research has established a foundation for practical, infrastructure-aware robot navigation. His exploration of deep reinforcement learning for robot control in human environments further demonstrates his commitment to creating robots that can operate safely and autonomously alongside people. Ismail’s work represents a significant step toward making autonomous robots truly independent of GPS and specialized hardware.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3WiFi Similarity-Based Odometry8 citations · 2023
- 4Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments8 citations · 2022
- 5