one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful

Very useful!

Great experience!

Congratulation all the people involved in this course!

Review :

Mentor Help: 0/5

Course Content: 4/5

Course Explanation: 4/5

Course Challenging: 4/5

Exercises : 3/5

Things which can be improved: There should be a programming exercise for each module especially for modules like ICP. There should be more mentor support as everything can't be understood by videos. There is/was an expectation of doing the final project in CARLA online but it was offline and also the ICP was pre-implemented. But overall for starters it is a very good course for state estimation to support and I strongly suggest to complete it if you aspire to be a self - driving car engineer.

There is no support for this class

The forums are almost useless and no teacher or staff ever answers anything on them

The lectures are pure fluff and hand-waving, no meat and no details

The projects are extremely difficult and there is no lessons to cover material needed for the projects

Would not recommend unless you want to basically learn on your own

Too much work BTW I did get 100%.

Got to learn about many concepts like least squares, Kalman filter, GNSS/INS sensing, LIDAR Sensing. Programming assignments were the most difficult part of this course. And definitely going towards the next course in the specialization.

I was impressed about the different methods available to do state estimation. The content was well presented (all slides shown are available as a PDF download) although in a quite compressed fashion. As in course 1 I would have preferred much longer videos so that more details of the different models could have been highlighted. Personally I was amazed about concepts like the Quarternion that I have never heart about before. A great plus from my perspective is that - like in course 1 - every lesson has a list of further articles to read - and in order to really comprehend the stuff presented I recommend in doing a deep-dive into these articles. Personally I found the coding assignments really demanding and as a side note I would have appreciated a little bit more presence of the teaching stuff to clarify. Currently the impression is that besides a monthly post in the discussion forum the teaching stuff is not visible - which is really sad as I think this whole specialization to be prime content. Unfortunately the locked video that will be shown to you when having completed the assignment is only a white screen and you are not able to follow the explanations the professor is providing. I would really appreciate if the invisible slides would be available for download but this is not the case. All in all I am a little bit mixed about the course as for example particle filters are just mentioned in one video but not explained as all the various types of Kalman filters. Still I give this course a 5-star ranking as it provides a good starting point for those trying to dig deeper into SLAM.

Sometimes hard, but still pretty much fun to solve all the problems :)

Could we use C++ to program the projects?

And also, in most assignments, please make sure every requirements and additional information are CORRECT and CLEAR! Now, some of them are REALLY MISLEADING!

The projects are useful enough

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

This is a fast paced course on state estimation. ES Kalman Filter is the focus of the final project. Lectures cover basics of Kalman filter very thoroughly. You need to spend quite some time to sort out complexity to finish the final project, yet the efforts are well spent. You will only graph the fundamentals after hard projects. Overall, a very well organized and executed course. Highly recommended.

Finishing this course was quite challenging, but I did it. Thanks a lot to the professors for the clear explanations.

Really Advanced and Challenging Course with great scope of gaining knowledge.

Very helpful!

An excellent course on state estimation and localization. This course is a hands-on approach to the development and implementation of the Kalman Filter for localization. Parts of the assignments and the final project were challenging and the course needs a lot of self-study. The resources provided on the course proved to be extremely useful throughout, and almost self-sufficient. I highly encourage anybody who's willing to take up a practical challenge in state-estimation to take this course.

Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion

From my point of view a very interesting and well prepared course.

excellent course with a lot of valuable and up to date information that is used in real modern self driving cars, it was challenging and very hard for me to go through but i assure you that it's worthy of the hard work required to pass it

it's really nice, and amazing course. I enjoyed it

best online course so far that explains kalman filter and estimation methods with examples not just focusing on theoretical ,Thanks to the Dr's and course staff who worked hard to produce this course.

excellent course

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.

great

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.

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.