Develop and industrialize multi-sensor fusion positioning systems, including LiDAR, IMU, and RTK.
Optimize the robustness and accuracy of LiDAR SLAM algorithms for complex environments such as mining and construction machinery.
Design tightly coupled or loosely coupled positioning systems integrating RTK and LiDAR SLAM to achieve drift-free global localization.
Deploy, tune, and engineer open-source algorithms such as Fast-LIO and FAST-LIO2.
Collaborate with hardware teams on sensor selection, calibration, and data synchronization.
Qualifications
Master's degree or higher in Computer Science, Automation, Surveying, Robotics, or related fields, with at least 2 years of hands-on experience in SLAM.
Solid understanding of LiDAR SLAM principles; hands-on experience with at least one open-source algorithm such as Fast-LIO, LOAM, or LIO-SAM, including real-world deployment.
Familiar with RTK/differential GPS principles and practical experience in fusing RTK with IMU/LiDAR for localization.
Strong C++ programming skills, proficient in ROS/ROS2, capable of independently implementing algorithms into production-ready systems.
Familiar with optimization libraries (Ceres, g2o, GTSAM, etc.) and point cloud libraries (PCL).
Good mathematical foundation (probability theory, linear algebra, Lie groups, and Lie algebras).
Industry experience in construction machinery, mining, or agricultural robotics is a plus.
Experience with NVIDIA CUDA or embedded platforms (e.g., NVIDIA Orin, RK3588) for algorithm deployment is a plus.
