Richard D. Hoobler
Papers
1
Total Citations
2
H-Index
1
About
Richard D. Hoobler is a researcher advancing the frontier of autonomous navigation, with a focused expertise in sensor fusion for small-scale robotic systems. His primary research areas encompass RGB-LiDAR integration, 3D bounding box estimation, and low SWaP-C (Size, Weight, Power, and Cost) indoor navigation applications. Hoobler’s most notable contribution is his development of a novel pipeline that combines RGB imagery with depth data to generate accurate 3D bounding boxes, specifically tailored for deployment on small form-factor unmanned aerial vehicles (UAVs). This work directly addresses the critical challenge of implementing robust perception systems under stringent hardware constraints, enabling autonomous indoor flight where traditional methods falter. While his seminal 2023 paper has garnered 2 citations, its significance lies in laying foundational groundwork for efficient, real-time object detection in resource-limited environments. Hoobler’s research is particularly impactful for the growing field of compact robotics, where balancing computational efficiency with spatial awareness is paramount. His achievements represent a practical step toward making autonomous navigation accessible in confined, indoor spaces, bridging the gap between high-performance algorithms and real-world deployment constraints.
Research Focus
Key Achievements
Top Papers
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