汽车系统的传感器融合与非线性滤波

Sensor Fusion and Non-linear Filtering for Automotive Systems

Learn fundamental algorithms for sensor fusion and non-linear filtering with application to automotive perception systems.

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瑞典查尔姆斯理工大学
edX
  • 完成时间大约为 6
  • 高级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Basics of Bayesian statistics and recursive estimation theory

Describe and model common sensors, and their measurements

Compare typical motion models used for positioning, in order to know when to use them in practical problems

Describe the essential properties of the Kalman filter (KF) and apply it on linear state space models

Implement key nonlinear filters in Matlab, in order to solve problems with nonlinear motion and/or sensor models

Select a suitable filter method by analysing the properties and requirements in an application

课程概况

In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.

The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox.

The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.

课程大纲

Section 1 - Introduction and Primer in statistics
Section 2 - Bayesian statistics (Week 1)
Section 3 - State space models and optimal filters (Week 1)
Section 4 - The Kalman filter and its properties (Week 2-3)
Section 5 - Motion and measurements models (Week 2-3)
Section 6 - Non-linear filtering (Week 4)
Section 7 - Particle filter (Week 5)

预备知识

Mathematical statistics and MATLAB.

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