Navid Mostofi

Papers

Total Citations

H-Index

About

Navid Mostofi is a robotics researcher specializing in autonomous navigation, 3D modeling, and visual perception for indoor environments. His most cited work, "RGB-D Indoor Plane-based 3D-Modeling using Autonomous Robot" (2014, 4 citations), introduces a system that leverages RGB-D sensors and plane-based feature extraction to construct detailed 3D models of indoor spaces. This contribution addresses a critical challenge in robotics: enabling remote users to quickly familiarize themselves with unfamiliar environments through rich, spatial data. Mostofi’s approach combines visual odometry with plane detection, enhancing the accuracy and efficiency of autonomous mapping. While his citation count reflects a focused, early-career impact, his work lays groundwork for applications in robot-assisted exploration, disaster response, and smart building management. By integrating low-cost sensors with robust algorithms, Mostofi demonstrates how autonomous systems can bridge the gap between raw sensor data and human-interpretable environmental models. His research continues to influence the development of lightweight, real-time 3D reconstruction techniques for resource-constrained robots.