Lakshay Sharma
Papers
2
Total Citations
47
H-Index
2
About
Lakshay Sharma is a leading researcher in autonomous off-road navigation, specializing in risk-aware perception and planning for ground robots. His work addresses the critical challenge of enabling robots to traverse unstructured, 3D terrain at high speeds while maintaining safety. Sharma’s major contributions include developing novel frameworks that integrate learning-based traversability assessment with real-time mapping and motion planning. His most-cited paper, “EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy” (2024, 36 citations), introduces a deep evidential approach that learns terrain properties directly from data, allowing robots to automatically identify good traction zones without manual cost design. This work is complemented by “RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation” (2023, 11 citations), which demonstrates how 2.5D maps can be leveraged for real-time, safe path planning. Sharma’s research has been recognized for its practical impact on autonomous systems, pushing the boundaries of how robots perceive and navigate complex off-road environments. His work is essential reading for students and researchers interested in field robotics, deep learning for perception, and risk-aware autonomy.
Research Focus
Key Achievements
Top Papers
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