Laurie Bose
Papers
6
Total Citations
139
H-Index
4
About
Laurie Bose is a robotics and computer vision researcher whose work centers on the development of ultra-efficient, low-latency vision systems for autonomous robots, with a particular focus on pixel processor arrays (PPAs) as transformative sensing hardware. Bose's most significant contribution lies in demonstrating how parallel processor arrays — devices that perform computation directly on the focal plane — can enable agile, real-time visual perception without the power and latency penalties of conventional computing architectures. With a cumulative citation count exceeding 130, Bose's research spans aerial and ground robotics, tackling critical challenges such as UAV target tracking, visual odometry in GPS-denied environments, and reactive navigation for non-holonomic mobile robots. The 2022 survey on sensor-level computer vision for agile robots (67 citations) stands as a landmark contribution, synthesizing the promise of PPAs for next-generation autonomous systems. Earlier work demonstrating vision-based UAV control using per-pixel processors (2017, 32 citations) established the foundational viability of this approach. Bose also advanced simulation frameworks by integrating the SCAMP vision system into robotic simulators, broadening accessibility for the research community. Collectively, this body of work positions Bose as a pioneering figure in the intersection of neuromorphic-style sensing and autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Sensor-level computer vision with pixel processor arrays for agile robots67 citations · 2022
- 2Tracking control of a UAV with a parallel visual processor32 citations · 2017
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- 6Bringing A Robot Simulator to the SCAMP Vision System2 citations · 2021