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YERTAYEV ARMAN
Mid-level Robotics & Computer Vision Engineer specializing in SLAM and edge AI
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Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.
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Key Strengths
- Replaced traditional CV feature extraction with deep learning approach
- Improved loop closure robustness in visually repetitive green environments via dataset collection and model training
- ROS1/ROS2 integration experience including ROS1-ROS2 bridging
- Point-cloud alignment using G-ICP to match differing sensor parameters
- Simulation-based testing using Gazebo with Docker
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