Max Robotics — Global Service Robot Directory + US Certification

About

Sergey Triputen’s research lies at the intersection of autonomous robotics, computer vision, and 3D reconstruction, with a particular focus on advancing monocular SLAM (Simultaneous Localization and Mapping) systems. His work addresses critical challenges in enabling robots to perceive and interact with unstructured, real-world environments. A key contribution is the development of a closed-form solution for converting IMU-based LSD-SLAM point clouds into scaled, real-world 3D coordinates—a fundamental step for bridging the gap between visual odometry and practical robotic manipulation. Triputen further explored the feasibility of using collaborative robots with monocular SLAM for accurate 3D object reconstruction, specifically targeting applications like bin-picking, where precise object geometry is essential for motion planning and grasp computation. His "Follow Me" project demonstrated real-time person tracking for autonomous robotics in dynamic, outdoor settings. While his citation counts (5, 3, and 2) reflect a focused, early-career impact, his work addresses a pressing need in the robotics community: making SLAM systems not just navigational tools, but reliable sources of metric, actionable spatial data for manipulation tasks. Triputen’s research is particularly relevant for engineers developing cost-effective, vision-based robotic systems for industrial automation and human-robot interaction.