The course covers some interesting and highly important concepts regarding state estimation. I guess the videos are not intended to be a "follow-along" lectures but more of a "these are the topics you should study by yourself" videos. In other words, the videos tend not to go deep, instead only the important results are quickly presented. On the other hand the programming assigments are quite fun.

Reflecting on how much knowledge and understanding somebody needs to show to pass this course I wouldn't rate it as advanced, I would rather say intermediate.

To sum up, this is either a course for somebody who wants to get some basic ideas about state estimation applied to self driving cars or for somebody who wants to dive deep into this topic and wants to use this course as a guidance on his/her self-study journey

The course has very advanced material and I value this course a lot. However I am very confused at some key concepts and didn't understand many details conceptually. For example it is not clear what is the difference between EKF and ES-EKF.

Also, for the final project the formulas have been given. I implemented the project using the formulas, but I didn't understand deeply enough the meaning of those formulas. For example what does Kalman Gain represent.

Maybe the topic is just so advanced, or maybe I should be reading more resources outside the lectures. But I finished the course with the feeling that I have a lot to learn in the space of localization and state estimation.

Great course that teaches you most of what you need to know about state estimation. What is missing is the state estimation using particle filter, it would be great if there is a module dedicated for that. Some video lectures are little bit confusing, specifically at the error state estimation part, but if you read the provided reading materials, you should be able to understand it more thoroughly. The final project is difficult, you are expected to read some advanced papers on state estimation, but it is very rewarding once you figure out on your own.

The course is a good fit for someone with some background in navigation. It would have been best if unnecessary content such as history of GNSS/GPS, history of Kalman filter and the "autobiographies'' in the beginning are truncated to make more time for the actual content.

For example one can easily google and learn about the history of GPS, Kalman, etc. This is the kind of material best left as a link to additional resources instead of the links to additional materials for Jacobian derivations and quaternion algebra.

This course is wonderful, however, is it quite tough, not only for the technical content but also because I believe it could use some more clarification for the quizzes and other.

All in all, I thought it was a very satisfying way to review old skills and learn new state-of-the-art techniques!

Recommending it heavily, but be ready for frustrations.

There are several errors in the presentations and in the videos, the tutors did not correct them and thus the assignments were very confusing due to stupid math mistakes made by the organizers, it is clear that they are not taking it 100% serious, nonetheless I have seen few courses were they explain State estimation for SDV so good as this one.

Great course! Very in depth understanding of Kalman Filters and Sensor Fusion. You need to look more literature to understand the concept. Final project is very nice. May be more insight could have been provided about orientation,quaternions and euler angles conversions.

A pretty involving course.

Good points - EKF, UKF explained properly.

Bad points - The weeks 3 onwards course is not sufficiently explained, less mathematics and more intuitive understanding, tough time if you do not have experience with python programming.

The content of the course is great, very useful and applicable ! The lectures are well told, animations are brilliant. I rate this course as 4 stars due to a low feedback activity from the teaching staff.

Thank you for this absolutely fantastic course. Kalman filters and state estimation in general is a concept that I've tried to understand for a long time, and I'm glad to have finally understood it!

A well-taught course by Prof. Jonathan Kelly.I accumulated huge amount of knowledge after undergoing his teachings.The supplementary readings proved to be of great help to ace the final project.

Challenging course, specially the assignments. The extra literature resources are great. The explanations and examples on the videos could improve. Step by step Hands On examples would fit great

For new learners, this course provides the beginner to intermediate knowledge. The explanation with examples are quite interesting and easy.

Content is great but lack of instructor support makes the course hard to understand.

the course really amazing the study material in this course was extremely in-depth.

Need more code example or supplementary reading about python and numpy

Some information was really difficult to understand.

Loved Every bit of it. Looking forward to get more

Excelente curso!

good!

Very informative about the definition and application about EKF at self driving car. However, I am a lidar engineer who want to know more mathematical and application details about how the lidar ToF data are translated to help with the localization, step by step...

On the other hand, videos kindly provided some of the derivation results of the ESEKF going to be implemented into final project. But the arithmetic process of the Quaternion calculation is quite confusing for the first-time learner and the professor didn't clearly explain the meaning of the algebras used in the videos, such as Cns, q(), capital omega, etc... which cost much unnecessary search time for me to figure them out.

Overall, this is a good course in Coursera Unlimited.

This course is better than looking up blogs and YouTube videos, but it does not create mastery over the subject. The content is "presented" via PowerPoint slides and leaves the student not feeling "taught." Stanford's State Estimation and Filtering for Robotic Perception is a far better course: https://online.stanford.edu/courses/aa273-state-estimation-and-filtering-robotic-perception. However, I dropped the course after two weeks because I lacked the math prerequisites at the time and subsequently, the course was too time-consuming for someone working full-time.

This course is good for those who are interested in learning about general concept (not in depth) of State Estimation. It would have been better if ICP topic had been distributed through a whole seperate week and had an coding assignment. One need to learn about Particle Filter too. Other than that I am satisfied .

The content and the projects are good, but a lot of details as derivations, mathematical concepts (like quaternions) and documentation of the project codes are not well covered neither in the course videos nor in the reading materials.

The forums were not very helpful to explain these details.

IMHO, assignments throughout all courses of the specialization are overcomplicated. I would appreciate more insights and intuitions about the subject rather than pain from calculation Jacobians and translating raw math formulas to Python code.