![]() ![]() Then, you’ll move onto using Markov localization in order to do 1D object tracking, as well as further leveraging motion models. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. After completing the course, you will have a solid foundation to work as a sensor fusion engineer on self-driving cars. You will get hands-on experience with multi-target tracking, where you will learn how to initialize, update and delete tracks, assign measurements to tracks with data association techniques and manage several tracks simultaneously. In the second half of the course, you will learn how to fuse camera and lidar detections and track objects over time with an Extended Kalman Filter. Also, you will learn how to detect objects such as vehicles in a 3D lidar point cloud using a deep-learning approach and then evaluate detection performance using a set of state-of-the-art metrics. You will learn about the lidar working principle, get an overview of currently available lidar types and their differences, and look at relevant criteria for sensor selection. Therefore, you will learn about the lidar sensor and its role in the autonomous vehicle sensor suite. Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability. In this course, you will learn about a key enabler for self-driving cars: sensor fusion. With this course, you will be exposed to the whole Machine Learning workflow and get a good understanding of the work of a Machine Learning Engineer and how it translates to the autonomous vehicle context. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models. In this course, you will develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. Python, C++, Linear Algebra and Calculus. ![]()
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