Arshad Jamal

Centre for Artificial Intelligence and Robotics

Papers

Total Citations

H-Index

About

Arshad Jamal is a robotics researcher whose work centers on enhancing autonomous navigation through improved environmental perception. His primary research areas include terrain mapping, sensor fusion, and robot locomotion in unstructured environments. Jamal’s major contribution is the development of HIET (Height Images for Enhanced Terrain), a novel approach that transforms sparse 3D-LiDAR or stereo camera point clouds into dense, image-like elevation representations. This method significantly improves a robot’s ability to perceive and traverse complex terrains, addressing a fundamental challenge in field robotics. While his most-cited paper, "HIET: Height Images for Enhanced Terrain Perception for Robot Navigation" (2024), is early in its citation life, it represents a promising step toward more robust off-road navigation. Jamal’s work bridges the gap between raw sensor data and actionable spatial understanding, enabling robots to better interpret slopes, obstacles, and ground roughness. His research is particularly relevant for applications in search-and-rescue, planetary exploration, and agricultural automation, where reliable terrain perception is critical for safe and efficient operation.