机器人SLAM算法工程师

该职位来源于猎聘 薪资面议 职位描述：

作为机器人 SLAM 开发软件工程师，您将负责同步定位与建图（SLAM）算法的研发与优化，持续提升扫地机器人产品的自主性与环境感知效能。您需与软件工程团队、机器人专家及硬件开发人员紧密协作，推动 SLAM 技术在产品端的工程化落地。 岗位职责：

• 算法研发: 负责家用清洁机器人场景下的SLAM算法研发与优化, 提升机器人的定位和建图性能 2. 多传感器融合: 设计实现基于lds, ToF,IMU,Wheel, 相机等多传感器融合的定位方案, 提升系统在复杂环境下的精度与鲁棒性 3. 系统集成与落地: 负责SLAM算法在机器人平台集成, 测试与性能优化, 确保SLAM算法在产品中的可靠性. 并协同导航-感知开发团队，确保SLAM系统的适配性. 4. 技术创新: 跟踪行业前沿技术,持续迭代算法体系,提升团队技术竞争力 任职要求：
• 计算机科学、软件工程等相关专业本科及以上学历，或具备同等工程实践经验 2. 熟悉Fast-LIO, Fast-LIVO2, ORB-SLAM, VINS等主流SLAM框架并具有实际项目经验 3. 精通图优化, 李群代数, PNP, 特征提取与匹配等核心算法, 熟悉ICP, NDT点云匹配, 点云滤波等数据处理方法 4. 熟练运用 C++/Python 进行实时机器人系统开发, 有嵌入式平台优化经验,有量产经历者优先 5. 熟悉机器人操作系统（ROS）、OpenCV、点云库（PCL）等开发工具链 6. 具有团队协作意识,责任心,乐于沟通分享,持续学习新技术 7. 英文口语可沟通（必须项）

Job Description As a Robotics SLAM Software Engineer, you will be responsible for the research, development, and optimization of Simultaneous Localization and Mapping (SLAM) algorithms to continuously enhance the autonomy and environmental perception capabilities of our robot vacuums. You will collaborate closely with software engineering teams, robotics specialists, and hardware developers to drive the productization and deployment of SLAM technologies. Key Responsibilities

• Algorithm R&D: Design, develop, and optimize SLAM algorithms tailored for home cleaning robot scenarios to improve localization accuracy and mapping performance.
• Multi-Sensor Fusion: Design and implement sensor fusion algorithms integrating LDS (LiDAR), ToF, IMU, wheel encoders, and cameras to boost system accuracy and robustness in complex household environments.
• Integration & Deployment: Integrate, test, and optimize SLAM algorithms on embedded robotics platforms to ensure system reliability. Collaborate with navigation and perception teams to ensure seamless system compatibility.
• Technology Innovation: Track cutting-edge industry developments and continuously iterate on algorithm architectures to maintain technical competitiveness. Qualifications & Requirements
• Bachelor’s degree or higher in Computer Science, Software Engineering, or related fields (or equivalent hands-on engineering experience).
• Practical project experience with mainstream SLAM frameworks such as Fast-LIO, Fast-LIVO2, ORB-SLAM, VINS, etc.
• Proficient in Graph Optimization, Lie Group & Lie Algebra, PnP, and feature extraction/matching. Familiar with point cloud processing techniques, including ICP, NDT, and point cloud filtering.
• Strong command of C++ and Python for real-time robotics development. Experience in performance optimization on embedded platforms; mass-production deployment experience is highly preferred.
• Proficient with ROS (Robot Operating System), OpenCV, PCL (Point Cloud Library), and related development tools.
• Excellent teamwork, strong sense of responsibility, good communication skills, and a strong drive for continuous learning.
• Fluent oral English skill (must)