Vikram Ramasamy

Papers

Total Citations

H-Index

About

Vikram Ramasamy is a leading researcher in robotics and GPU-accelerated perception, whose work bridges the gap between computationally constrained hardware and real-time dense mapping for autonomous systems. His most-cited paper, "nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping" (2024, 23 citations), addresses a critical challenge in robotics: enabling low-latency, volumetric mapping onboard robots with limited computational resources. By leveraging GPU acceleration, Ramasamy’s approach overcomes the limitations of traditional CPU-based systems, allowing robots to generate dense, signed distance field maps in real time—a foundational capability for navigation and environmental interaction. This work has already garnered significant attention for its practical impact on autonomous systems, from drones to ground vehicles. Ramasamy’s contributions are particularly notable for their focus on efficiency and scalability, making advanced mapping techniques accessible to resource-constrained platforms. His research sits at the intersection of robotics, computer vision, and high-performance computing, with implications for fields ranging from industrial automation to search-and-rescue operations. As a rising voice in the robotics community, Ramasamy continues to push the boundaries of what is possible in real-time spatial understanding.