Key Responsibilities
1. Lead research and development of 2D LiDAR-based SLAM algorithms for unknown environment mapping and self-localization in floor cleaning robots.
2. Design and optimize motion distortion correction algorithms and multi-sensor fusion solutions (LiDAR/IMU/odometry).
3. Develop and refine back-end optimization algorithms (e.g., Bayesian filtering, graph optimization, branch-and-bound methods) for robust SLAM performance.
4. Innovate relocalization algorithms to recover from localization failures in dynamic environments.
5. Research 2D point cloud feature extraction methods, including clustering algorithms for structured/unstructured environments
职位要求：
Requirements
Technical Expertise:
61 Strong knowledge of LiDAR SLAM core components: scan matching, loop closure detection, Bayesian filtering, and map optimization.
61 Hands-on experience with SLAM frameworks: Cartographer, KartoSLAM, or Gmapping.
61 Proficiency in sensor integration: deep understanding of LiDAR/IMU/odometry principles and fusion techniques (Kalman filters, particle filters).
Development Skills:
61 Proficient in C++ and Python with clean coding practices.
61 Experience with ROS/ROS2 for robotic system development.
61 Demonstrated work on 2D point cloud processing (feature extraction, descriptor matching).
Preferred Qualifications:
61 3+ years of experience in mobile robot SLAM development (household/service robots preferred).
61 Publications or patents in SLAM-related fields.
61 Fluency in spoken English for global team collaboration.
Education:
61 Bachelor’s/Master’s degree in Automation, Computer Science, Mathematics, or related fields.