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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-kalman-filtering-and-sensor-fusion/
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课程名称：高级卡尔曼滤波与传感器融合 课程概述： 本课程旨在教授传感器融合和卡尔曼滤波的基本概念，重点关注自动驾驶车辆中这些概念的应用。卡尔曼滤波是估计和数据融合理论史上最伟大的发现之一，20世纪最重要的工程发现之一，推动了许多技术的进步，包括复杂动态系统（如汽车、飞机、船舶和航天器）的控制。 学习传感器融合和卡尔曼滤波的原因： 数据融合是现代技术中几乎无处不在的工具，涉及任何形式的传感、测量或自动化。卡尔曼滤波是数据融合中最广泛使用的方法之一，对这一过程的理解将使学习更复杂的方法变得更容易。传感器融合是卡尔曼滤波的关键用途，广泛应用于无人驾驶车辆和自驾车中。课程会提供关于卡尔曼滤波评估和调优的实用技巧，帮助学员避免常见的调试问题。 课程内容： - 从基础开始学习概率和系统理论 - 学习线性卡尔曼滤波、扩展卡尔曼滤波和无迹卡尔曼滤波 - 探讨传感器融合的高级主题，如故障检测和传感器误差建模 - 实现C++仿真，解决自动驾驶汽车传感器融合问题 - 课程结束时的顶点项目是实施无迹卡尔曼滤波器，并模拟其在真实自驾车或自动驾驶车辆中的使用。 学习成果： - 使用线性卡尔曼滤波解决线性最优估计问题 - 使用扩展卡尔曼滤波解决非线性估计问题 - 使用无迹卡尔曼滤波解决非线性估计问题 - 聚合多个传感器的测量，处理不同更新频率 - 调优卡尔曼滤波以实现最佳性能 - 正确初始化卡尔曼滤波以实现稳健运行 - 在卡尔曼滤波中建模传感器误差及使用故障检测移除不良测量 - 在C++中实现上述三种卡尔曼滤波器的变体 课程要求： - 基础微积分、线性代数、概率知识 - 了解C++编程 适合人群： - 大学学生或独立学习者 - 有志于成为机器人或自驾车工程师的爱好者 - 工程师和科学家 - 希望了解与卡尔曼滤波和传感器融合相关数学理论和技能的工程专业人士 - 渴望理解数据融合基本概念以支持开发数据融合代码的软件开发人员 课程内容包括： > 8小时视频讲座，包含解释、示例、图画、图表和动画 > 重要笔记和练习的PDF备忘单 > 自驾车示例的C++仿真代码 > 所有源代码及友好的答疑支持 讲师资格： 讲师在航空航天和自动化公司工作了十年，担任引导、导航和控制工程师，专注于飞机、导弹和车辆状态估计的传感器融合。本课程内容曾教授给本科生、硕士生和博士生以及工程专业人士。 欢迎观看课程介绍视频和免费样本，了解课程内容。如果您认为这门课程对您有帮助，请注册，若课程不适合您可申请退款。期待在课程中见到您！
You need to learn know Sensor Fusion and Kalman Filtering! Learn how to use these concepts and implement them with a focus on autonomous vehicles in this course.The Kalman filter is one of the greatest discoveries in the history of estimation and data fusion theory, and perhaps one of the greatest engineering discoveries in the twentieth century. It has enabled mankind to do and build many things which could not be possible otherwise. It has immediate application in control of complex dynamic systems such as cars, aircraft, ships and spacecraft.These concepts are used extensively in engineering and manufacturing but they are also used in many other areas such as chemistry, biology, finance, economics, and so on.Why focus on Sensor Fusion and Kalman FilteringData Fusion is an amazing tool that is used pretty much in every modern piece of technology that involves any kind of sensing, measurement or automation.The Kalman Filter is one of the most widely used methods for data fusion. By understanding this process you will more easily understand more complicated methods.Sensor fusion is one of the key uses of Kalman Filtering and is extensively used in unmanned vehicles and self-driving cars.Evaluating and tuning the Kalman Filter for best performance can be a bit of a 'black art', we will give you tips and a structure so you know how to do this yourself.So you don't waste time trying to solve or debug problems that would be easily avoided with this knowledge! Become a Subject Matter Expert!What you will learn:You will learn the theory from ground up, so you can completely understand how it works and the implications things have on the end result. You will also learn practical implementation of the techniques, so you know how to put the theory into practice. In this course you will work with a C++ simulation that leads you through the implementation of various Kalman filtering methods for autonomous vehicles.At the end of the course, the Capstone project is to implement the Unscented Kalman Filter and run it as it would be used in a real self-driving car or autonomous vehicle!We will cover:Basic Background Probability and Systems TheoryLinear Kalman FilteringExtended Kalman FilteringUnscented Kalman FilteringAdvanced Topics for Sensor Fusion, such as fault detection and sensor error modelling.C++ Implementation in simulation for a self-driving car sensor fusion problem.By the end of this course you will know:How to use the Linear Kalman Filter to solve linear optimal estimation problemsHow to use the Extended Kalman Filter to solve non-linear estimation problemsHow to use the Unscented Kalman Filter to solve non-linear estimation problemsHow to fuse in measurements of multiple sensors all running at different update ratesHow to tune the Kalman Filter for best performanceHow to correctly initialize the Kalman Filter for robust operationHow to model sensor errors inside the Kalman FilterHow to use fault detection to remove Bad Sensor measurementsHow to implement the above 3 Kalman Filter Variants in C++How to implement the LKF in C++ for a 2d Tracking ProblemHow to implement the EKF and UKF in C++ for an autonomous self-driving car problemWhat are the course requirements or prerequisites:This course is part of the more advanced series and as such it does have a few prerequisites:Basic Calculus: Functions, Derivatives, IntegralsLinear Algebra: Matrix and Vector OperationsBasic ProbabilityBasic C++ Programming KnowledgeWho is this course for:University students or independent learners.Aspiring robotic or self-driving car engineers or enthusiasts.Working Engineers and Scientists.Engineering professionals who want to brush up on the math theory and skills related to Kalman filtering and Sensor Fusion.Software Developers who wish to understand the basic concepts behind data fusion to aid in implementation or support of developing data fusion code.Anyone already proficient with the math "in theory" and want to learn how to implement the theory in code.What you will get in this course:>8 hours of video lectures that include explanations and walk thoughts, pictures, diagrams and animations.PDF documents of cheat sheets with important notes and exercisesC++ simulation code for a self driving car example.All the source code and friendly support in the Q & A area.Why am I qualified to teach this course:I have been employed for the last decade as a Guidance, Navigation and Control engineer for a number of aerospace and automation companies, focusing on sensor fusion for aircraft, missile and vehicle state estimation. I have taught this content to bachelor's, master's and PhD students while teaching at university and to engineering professionals.So what are you waiting for??Watch the course instruction video and free samples so that you can get an idea of what the course is like. If you think this course will help you then sign up, money back guarantee if this course is not right for you.I hope to see you soon in the course!Steve
