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About

Manthan Patel is a robotics researcher whose work spans autonomous navigation, collaborative SLAM, and robot perception, with particular expertise in tackling the challenges of complex, unstructured environments. His most influential contribution, "RoadRunner" (2024, 31 citations), addresses traversability estimation for high-speed autonomous off-road driving, enabling robots to interpret degraded sensory conditions and sparse geometric data using onboard sensing alone — a critical advancement for real-world deployment. Building on this, his follow-up work "RoadRunner M&M" extends these capabilities to multi-range, multi-resolution terrain understanding. Patel also made significant contributions to multi-robot systems through "COVINS-G" (2023, 27 citations), a flexible back-end framework for collaborative Visual-Inertial SLAM that enables teams of robots to co-localize within a shared reference frame. His research further extends to search-and-rescue robotics, demonstrated through LiDAR-guided object detection in subterranean environments (2022), and large-scale dataset development via TartanGround (2025), supporting the broader perception and autonomy research community. His early work on FPGA-based fruit harvesting reflects a versatile foundation in applied robotics. With over 70 citations across his career, Patel is an emerging force in field robotics and autonomous systems research.