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Open nowPosted 400 days ago

空间智能（vslam）

zcnma1sv5kma/index41 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now空间智能（vslam）zcnma1sv5kma/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

13.6% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.3%1 day
  2. 4.7%3 days
  3. 13.6%7 days
  4. 21.9%14 days
  5. 36.9%30 days
  6. 59.0%60 days
This job: posted 400 days ago

The posting

岗位职责 多传感器融合 SLAM 算法研发：负责多目视觉与 IMU 紧耦合融合的 SLAM/VIO 算法研发、性能优化及工程落地，提升复杂动态场景下的定位与建图精度、鲁棒性和实时性。 学习型与端到端 SLAM 算法研发：负责基于深度学习的 SLAM 前端算法及端到端 SLAM 技术研发，探索学习方法与传统几何优化框架的融合，提升复杂场景下的特征感知、匹配、跟踪与位姿估计能力。 3D 空间真值构建与评估算法研发：负责三维空间位姿、几何结构及运动轨迹等高精度真值的构建与评估算法研发，探索多传感器融合、光学定位与几何约束等技术在真值生成、精度校验、误差评估及轨迹优化中的应用。 空间智能前沿技术探索：参与三维场景重建与表示（NeRF、3D Gaussian Splatting）、空间感知、语义场景理解等前沿方向的研究、技术验证与原型开发，推动相关技术在机器人及具身智能场景中的应用。

必备条件 扎实的计算机视觉、机器人定位与建图基础，熟悉 VSLAM/VIO 核心原理，掌握多视图几何、相机成像与标定、非线性优化、图优化等相关知识。 熟悉 VINS-Fusion、ORB-SLAM3 等一种或多种主流开源 SLAM 框架，具备算法阅读、调试、改进或独立开发能力。 具备视觉 SLAM 在机器人或其他实际设备上的部署与应用经验，能够针对真实场景中的定位退化、跟踪失败、动态干扰及实时性等问题进行分析与优化。 熟悉 C++ 或 Python，具备良好的算法实现、实验验证与工程调试能力。 优先考虑 具有动捕系统、红外 LED 光学定位、主动光学标记追踪或高精度三维位姿测量相关项目经验。 具有多目视觉与 IMU 融合、学习型视觉前端、端到端 SLAM 或相关深度学习算法研发经验。 具有 NeRF、3D Gaussian Splatting、三维场景重建与表示、空间感知或具身智能等空间智能方向的研究或项目经验。 在相关领域发表过高质量论文，或拥有有影响力的开源项目、技术成果及实际落地经验。

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