Mayank Bansal

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About

Mayank Bansal’s research lies at the intersection of autonomous navigation, mobile robotics, and real-time 3D perception, with a strong focus on enabling intelligent vehicles to operate safely in complex environments. His most cited work, “A LIDAR streaming architecture for mobile robotics with application to 3D structure characterization” (12 citations), introduced a novel two-level 3D data structure that allows pipelined, on-board processing of streaming LIDAR data from moving robots—a key contribution for real-time autonomy. In “Vision-based Perception for Autonomous Urban Navigation” (8 citations), Bansal helped build a low-cost vision-based sensing system that integrates driver awareness algorithms with commercial hardware, advancing the feasibility of autonomous driving in urban traffic. His work on “Lidar-based door and stair detection from a mobile robot” (3 citations) further demonstrates his ability to solve practical perception challenges, such as real-time detection of indoor obstacles. Collectively, Bansal’s contributions have shaped efficient, on-the-move sensing architectures that balance computational constraints with robust environmental understanding, making him a notable figure in field robotics and autonomous vehicle research.