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About

Arun Das is a researcher whose work lies at the intersection of autonomous robotics, computer vision, and machine learning, with a particular focus on enabling robust perception in challenging, GPS-denied environments. His most significant contribution is in the field of autonomous exploration, where his 2013 paper on mapping, planning, and sample detection strategies (42 citations) proposed an integrated solution for simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. Das has also made notable advances in multi-camera visual SLAM, pioneering the concept of dynamic camera clusters (DCCs) that allow for moving camera configurations—a departure from traditional fixed-calibration systems. His work on calibrating these dynamic clusters (14 citations) and his investigation into "Taming the North" for multi-camera tracking in snow-laden environments (7 citations) highlight his dedication to real-world, field-deployable systems. More recently, Das has expanded into the safety-critical domain of learning-based control, studying adversarial attacks on behavioral cloning dynamics (2020). Through these contributions, he has established himself as a researcher who not only pushes algorithmic boundaries but also rigorously tests his systems in harsh, real-world conditions.