Aniruddh Sikdar

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About

Aniruddh Sikdar is a rising researcher at the forefront of robotic perception and multi-spectral vision systems, with a focus on enhancing safety-critical autonomous navigation. His primary research areas include low-light thermal imagery, knowledge distillation, and semantic segmentation for aerial robotics. Sikdar’s major contribution, the SKD-Net (Spectral-based Knowledge Distillation Network), addresses a critical challenge: enabling robust semantic segmentation in adverse, low-light environments where traditional electro-optical sensors fail. By leveraging multi-spectral fusion and spectral knowledge transfer, his work significantly improves the generalization capacity of aerial perception systems, making them more reliable for real-world deployment in search-and-rescue, surveillance, and autonomous driving. Though his career is early-stage, his 2024 paper has already garnered 5 citations, signaling growing recognition in the computer vision and robotics communities. Sikdar’s innovative approach to bridging the gap between thermal and visible spectrum data positions him as a promising contributor to the next generation of resilient, all-weather robotic perception. His work not only advances academic understanding but also holds tangible implications for safety in autonomous systems operating under challenging visual conditions.