Expertise in various SLAM techniques, such as visual SLAM, LiDAR SLAM, visual-inertial SLAM, graph-based SLAM, Kalman filtering, bundle adjustment, structure from motion, etc.
Proficiency in programming languages commonly used in SLAM, such as C++ and Python.
Solid understanding of computer vision, sensor fusion, probabilistic robotics, and optimization algorithms.
Experience with popular SLAM libraries and frameworks, such as ROS, OpenCV, PCL, GTSAM, and g2o.
Strong mathematical and analytical skills, with the ability to apply advanced mathematical concepts to solve SLAM challenges.
Prior experience working with autonomous systems and robotics.
Lead the design and development of SLAM-based navigation systems for robotic platforms