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Location
In office (China)
Seniority
Junior · 2+ years exp

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Oct 12, 2026. JD.com scores A on the Alion truth index.

Overview
Company
Impact
Profile match
JD.com is a Chinese retailer and logistics operator founded in 1998 by Richard Liu, which moved online in 2004 and built its business on first-party inventory rather than a pure marketplace model. Unlike its peers it owns most of the goods it sells and runs its own nationwide warehouse and delivery network, which gives it tight control over authenticity and same-day or next-day fulfilment for hundreds of millions of customers. The group has expanded into logistics services for third parties, healthcare, industrial supply, cloud computing and consumer finance, and is listed in both New York and Hong Kong.

岗位职责 / Responsibilities

1. 负责物流配送无人机视觉 SLAM / VIO 算法研发,基于单目/双目/深度相机融合 IMU 与 GNSS,实现无 GNSS/弱 GNSS 环境下的实时定位与局部地图构建。

2. 设计并实现视觉-惯性-卫星多源融合的紧耦合状态估计框架,融合多视图几何约束、IMU 预积分与 GNSS 观测,基于 MSCKF、VINS-Mono/Fusion、ORB-SLAM3 等方案在 ESKF / 滑动窗口优化框架中完成高精度定位。

3. 面向物流配送多场景(低光照、纹理稀疏、高速运动、悬停起降、楼宇阴影交替),解决视觉退化问题(特征丢失、尺度漂移、动态遮挡),设计光照鲁棒的特征提取(SuperPoint / D2-Net)与数据关联策略。

4. 通过位姿图优化消除长航时累积漂移,支持无人机多趟配送的重复定位与地图复用。

5. 研究并实现基于先验地图(点云地图、特征地图、语义地图)的视觉定位,探索图像与卫星影像匹配、大规模图像检索、视觉地理配准等前沿技术以增强全局定位能力。

6. 设计并实现多传感器间动静态标定算法(相机内参、相机-IMU、相机-相机外参),推进在线标定与无目标标定等量产级方案落地。

7. 面向量产需求,将 SLAM 算法部署至嵌入式 AI 平台(NVIDIA Jetson / 高通 RB5 / 地平线 J5),完成模型量化、加速与前/后端实时性优化,并参与代码框架设计与 CPU/GPU 性能优化。

8. 协同感知、组合导航团队实现视觉+GNSS+IMU 多源融合方案,确保各类场景下定位的无缝切换与连续输出; 与硬件、软件、测试团队紧密合作,分析和解决量产项目中的定位问题。

9. 跟踪领域前沿进展(IROS、ICRA、RAL、TRO、IJRR、JFR 等顶级会议期刊),将可行先进算法进行工程化转化。

任职要求 / Requirements

1. 硕士以上学历,电子工程、计算机、自动化、机器人、遥感测绘、图像处理等相关专业; 3 年以上视觉 SLAM / VIO 算法研发经验,含 2 年以上 SLAM 量产项目经验。

2. 精通多视图几何、相机模型与标定、非线性优化(LM / Dogleg)、视觉 SLAM 核心算法。

3. 熟练掌握 C/C++,熟练使用 ROS/ROS2 及 pcl、eigen、opencv、ceres、gtsam、g2o 等常用开源库,具备实际无人机/机器人视觉定位部署经验。

4. 熟悉主流 VIO / 视觉 SLAM 框架(VINS-Mono/Fusion、ORB-SLAM3、DROID-SLAM、OpenVINS 等)并至少深入理解其一,了解滑窗优化、IMU 预积分、回环检测、特征跟踪或直接法等核心技术; 熟悉视觉、IMU、GNSS 传感器特性并能进行噪声建模。

5. 以下方向至少满足其一(多项符合者优先):

  • 方向一(GVIO 融合):熟悉经典 VIO 算法框架(VINS、MSCKF、OpenVINS、SVO、DSO 等);
  • 方向二(先验地图定位):熟悉基于 Tag(AprilTag / ArUco)定位,或基于 NDT/矢量先验地图定位,或视觉/激光重定位,有地图矢量化、地图融合量产经验,或熟悉图像与卫星影像匹配、图像检索、地理配准算法;
  • 方向三(标定算法):熟悉传感器内外参标定,有在线标定与无目标标定量产经验。

符合京东价值观:客户为先、创新、拼搏、担当、感恩、诚信。

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