文远知行SLAM算法工程师（2026届校招）
校招全职其他地点：北京 | 深圳 | 广州状态：招聘
任职要求
• 计算机、自动化、电子工程、导航工程、地理信息系统、遥感、测绘等相关专业本科及以上学历，硕士及以上优先 • 良好的数学、算法基础以及编程经验，熟悉C++或Python • 熟悉Linux系统环境下的操作和开发 • 能够在充满未知因素和快节奏的环境中工作，结果导向 • 踏实勤奋、积极主动、好学上进，有团队合作精神 • 逻辑清晰，具有良好的表达与沟通能力 • 有高水平会议、知名期刊论文发表者优先 • 有自动驾驶、机器人、无人机背景者优先 具有下列至少一项背景或经验，有多项匹配者优先： • 导航定位，基于隐马尔科夫模型(HMM)的地图匹配 • 基于IMU、轮速计等传感器的高精度车辆航位推算(Dead Reckoning) • 3D计算机视觉、SLAM、VIO。熟悉SLAM相关软件或代码库：ROS、Eigen、Ceres、g2o、GTSAM等至少一种 • 机器学习算法理论及实践，包括深度学习（熟悉主流框架，TensorFlow、Torch、MXNet等至少一种）、经典机器学习算法（GBDT、SVM等）。有图像、视频、点云等视觉数据的检测、识别、分割等项目经验更佳 • 机器人学、状态估计理论、实时多传感器数据处理和融合（GNSS、IMU、轮速计等）、EKF/PF/UKF等滤波技术、优化算法包括因子图优化等
工作职责
北京或广州或深圳 与地图及定位系统相关的，基于相机、lidar、GNSS、轮速计、IMU等多种传感器的各种智能算法的研发。工作涉及但不限于：多传感器融合建图和定位算法开发、传感器校准、点云数据处理、地图元素自动识别和智能标注等。
包括英文材料
GIS+
https://www.osgeo.org/resources/learn-gis-free-complete-course/
Learning GIS, especially a modern GIS approach, can seem overwhelming, but this video explains how to take a four-step process to learn modern GIS and some tools to help you get started!
https://www.youtube.com/watch?v=n9dDsYLIx1c
Learning GIS, especially a modern GIS approach, can seem overwhelming, but this video explains how to take a four-step process to learn modern GIS and some tools to help you get started!
学历+
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
自动驾驶+
https://www.youtube.com/watch?v=_q4WUxgwDeg&list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr
Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen)
https://www.youtube.com/watch?v=NkI9ia2cLhc&list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
SLAM+
https://docs.mrpt.org/reference/latest/tutorial-slam-for-beginners-the-basics.html
[英文] SLAM for Dummies
https://dspace.mit.edu/bitstream/handle/1721.1/119149/16-412j-spring-2005/contents/projects/1aslam_blas_repo.pdf
A Tutorial Approach to Simultaneous Localization and Mapping
https://ouster.com/insights/blog/introduction-to-slam-simultaneous-localization-and-mapping
SLAM is an essential piece in robotics that helps robots to estimate their pose – the position and orientation – on the map while creating the map of the environment to carry out autonomous activities.
[英文] What Is SLAM?
https://www.mathworks.com/discovery/slam.html
How it works, types of SLAM algorithms, and getting started
ROS+
https://www.youtube.com/watch?v=92Zz5nnd41c&list=PLk51HrKSBQ8-jTgD0qgRp1vmQeVSJ5SQC
https://www.youtube.com/watch?v=HJAE5Pk8Nyw
Ready to learn ROS2 and take your robotics skills to the next level?
https://www.youtube.com/watch?v=MWKnMPX0Yjg&list=PLU9tksFlQRircAdEplrH9NMm4WtSA8yzi
Do you want to know more about ROS the Robot Operating System?
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
MXNet+
https://www.tutorialspoint.com/apache_mxnet/index.htm
Apache MXNet is a powerful deep learning framework that supports both symbolic and imperative programming.
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
相关职位
校招研发算法类
关于文远知行：文远知行WeRide是全球领先的L4级自动驾驶科技公司，已经在全球23个城市开展自动驾驶研发、测试及运营。文远知行致力于开发安全可靠的无人驾驶技术，形成了自动驾驶出租车、小巴、同城货运车、环卫车、高阶智能驾驶等五大产品矩阵，提供网约车、随需公交、同城货运、智能环卫、解决方案供应商等多种服务。 和智能出行专家团队一起工作，推动人工智能的前沿发展， 研发用于自动驾驶的计算机视觉，机器学习， 深度学习， 强化学习等。 技术方向包括但不限于： 传感器的标定与融合，物体检测跟踪及预测， 可行驶区域检测， SLAM，3D点云处理，路径规划，以及控制优化等。
更新于 2025-06-19
校招其他
关于我们人工智能模型的表现很大程度上依赖于高质量的训练数据。文远知行的数据团队拥有专业的数据标注团队和系统，我们的算法团队致力于通过先进的模型和算法通过自动和半自动标注提高标注效率和质量。 构建自动驾驶4d labeling系统和算法，开发多传感器或纯视觉的动静态对象自动标注算法。
更新于 2025-06-19
实习集团
1、前沿技术研究与应用： （1）深入研究并实践三维建模领域的最新技术，包括但不限于NeRF（神经辐射场）建模、3D Gaussian Splatting等高级建模算法； （2）对传统photogrammetry pipeline有深入理解，优化并提升高精度相机位姿估计、SFM（Structure from Motion）、MVS（Multi-View Stereo）、SLAM（Simultaneous Localization and Mapping）等算法的性能； 2、3D图像优化与工程化： （1）主导或参与3D图像优化pipeline的设计与实施，解决阴影、日光处理等视觉问题，确保3D应用的真实感和用户体验； （2）将研究成果工程化落地，确保技术能够在产品中得到有效应用并持续迭代优化； 3、隐式三维模型编辑技术研发： （1）负责隐式三维模型编辑技术的研发，包括模型压缩、材质与光照解耦、显式与隐式模型融合等，提升模型编辑的灵活性和效率； （2）探索并应用新的技术和算法，不断优化三维模型的质量和处理速度； 4、AI驱动的自动化与效率提升： 利用人工智能和机器学习技术，设计并实施自动化解决方案，提升三维建模与编辑过程的效率和准确性，降低人工干预成本。
更新于 2025-04-03
实习
岗位职责： 1. 协助设计和实现多传感器融合的SLAM算法，优化算法在不同环境下的鲁棒性和实时性能。 2. 参与单目和多目VIO系统的研发，实现预积分、零偏估计等核心模块，提升系统准确性和稳定性。 3. 参与感知定位算法在人形机器人上的落地交付, 确保算法性能满足业务需求。 4. 编写技术文档，参与团队内部的技术交流和讨论。
更新于 2025-07-01