À propos de ce cours
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100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.

Approx. 24 heures pour terminer

Recommandé : 4 weeks of study, 5-6 hours per week...

Anglais

Sous-titres : Anglais

Ce que vous allez apprendre

  • Check

    Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares

  • Check

    Develop a model for typical vehicle localization sensors, including GPS and IMUs

  • Check

    Apply extended and unscented Kalman Filters to a vehicle state estimation problem

  • Check

    Apply LIDAR scan matching and the Iterative Closest Point algorithm

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.

Approx. 24 heures pour terminer

Recommandé : 4 weeks of study, 5-6 hours per week...

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
2 heures pour terminer

Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars

9 vidéos (Total 33 min), 3 lectures
9 vidéos
Welcome to the Course3 min
Meet the Instructor, Jonathan Kelly2 min
Meet the Instructor, Steven Waslander5 min
Meet Diana, Firmware Engineer2 min
Meet Winston, Software Engineer3 min
Meet Andy, Autonomous Systems Architect2 min
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5 min
The Importance of State Estimation1 min
3 lectures
Course Prerequisites: Knowledge, Hardware & Software15 min
How to Use Discussion Forums15 min
How to Use Supplementary Readings in This Course15 min
7 heures pour terminer

Module 1: Least Squares

4 vidéos (Total 33 min), 3 lectures, 3 quiz
4 vidéos
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6 min
Lesson 2: Recursive Least Squares7 min
Lesson 3: Least Squares and the Method of Maximum Likelihood8 min
3 lectures
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45 min
Lesson 2 Supplementary Reading: Recursive Least Squares30 min
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30 min
3 exercices pour s'entraîner
Lesson 1: Practice Quiz30 min
Lesson 2: Practice Quiz30 min
Module 1: Graded Quiz50 min
Semaine
2
7 heures pour terminer

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6 vidéos (Total 53 min), 5 lectures, 1 quiz
6 vidéos
Lesson 2: Kalman Filter and The Bias BLUEs5 min
Lesson 3: Going Nonlinear - The Extended Kalman Filter9 min
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6 min
Lesson 5: Limitations of the EKF7 min
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15 min
5 lectures
Lesson 1 Supplementary Reading: The Linear Kalman Filter45 min
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10 min
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45 min
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1 h
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30 min
Semaine
3
2 heures pour terminer

Module 3: GNSS/INS Sensing for Pose Estimation

4 vidéos (Total 34 min), 3 lectures, 1 quiz
4 vidéos
Lesson 2: The Inertial Measurement Unit (IMU)10 min
Lesson 3: The Global Navigation Satellite Systems (GNSS)8 min
Why Sensor Fusion?3 min
3 lectures
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10 min
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30 min
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15 min
1 exercice pour s'entraîner
Module 3: Graded Quiz50 min
Semaine
4
2 heures pour terminer

Module 4: LIDAR Sensing

4 vidéos (Total 48 min), 3 lectures, 1 quiz
4 vidéos
Lesson 2: LIDAR Sensor Models and Point Clouds12 min
Lesson 3: Pose Estimation from LIDAR Data17 min
Optimizing State Estimation3 min
3 lectures
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10 min
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10 min
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30 min
1 exercice pour s'entraîner
Module 4: Graded Quiz30 min
4.6
34 avisChevron Right

Meilleurs avis pour State Estimation and Localization for Self-Driving Cars

par WSOct 14th 2019

There are many interesting topics. Without the help and suggested readings from this course, I wouldn't be able to finish by myself. Also, the final project is very enlightening.

par RLApr 27th 2019

It provides a hand-on experience in implementing part of the localization process...interesting stuff!! Kind of time-consuming so be prepared.

Enseignants

Avatar

Jonathan Kelly

Assistant Professor
Aerospace Studies
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Steven Waslander

Associate Professor
Aerospace Studies

À propos de Université de Toronto

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

À propos du Spécialisation Voiture autonome

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
Voiture autonome

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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