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%.

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.

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

Very dry lectures!

Quiz automated grader buggy and not working at times. Example: not well defined python environment for the quiz in module 4. A grader expects a certain format that you have to guess. But to guess you need to submit the quiz in order to see if you satisfied the grader. So you can do that 5 times every our. A lot of time spent on satisfying the grader format that learning material.

The reason I am realty trying to stay in the class is because I am very interested in the subject but the execution of this class is a disaster!

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.

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.

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

Excellent course! The best course available of this topic

a very good course about sensor fusion ans localization

Very helpful!

great

Great coverage of state estimation without getting the student bogged down in gratuitous math and proofs. The videos cover enough for you to implement the algorithms and the referenced additional readings will help guide you to learn more depth on the subject if you want (I think it is extremely important to read some of these materials). Simplifying assumptions are stated in the videos so that the student understands how this material deviates from more realistic or advanced implementations - this is a very important point so I was glad to see it clearly explained.

As a critical comment, I would have liked to a little more detail about quaternions to help improve intuition about them. Quaternions are likely new to a lot of people, and they are certainly not intuitive.

Amazing course. Highly recommend for understanding some of the foundational blocks for self-driving cars including some workhorse algorithms/principles that are used. Definitely a "weed"-out course and the student has to persevere and draw upon the resources provided in the readings and other resources outside to be able to completely internalize the concept and the math that explains the concepts. However, for students that are willing to put in the effort, there is a lot of learning to be gained on the other side. As a suggestion, the course, should be extended to 7 weeks with some more in-depth discussion into the derivations - specifically on the Unscented Kalman Filter. A guided discussion on one or two fundamental papers will also be fantastic.

A very thoughtful introduction to the subject of state estimation and localization. The material introduces sufficient basic material and in adequate depth to equip you to learn more. Don't expect to be writing production level code after finishing this course. The expectation should be to learn enough to venture in the field of state estimation on your own and to be able to understand the material in books, research papers and other resources. The supplementary resources are extremely well selected and provide very good pointers to deepen your knowledge. The exercises are definitely very helpful.

This course was really amazing! The supplementary material provided really helped me to strengthen my concepts. The lectures were very clear and to the point. This course demands hard work from the student's side too, unlike many other courses on Coursera which literally spoon-feed everything. The discussion forums are really helpful if you use them properly. The programming assignments are challenging and might even leave you frustrated at times. But overall, it's worth the time. I highly recommend this course to people who are interested in self-driving cars

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.

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.

This course provides a lot of insights in various sensors used for pose estimation and also delves into multi sensor fusion which gives the knowledge and importance about the sensor calibration. Overall a very well taught course and the most important one for who want to pursue a career in self driving cars.

Firstly, I would like to start thanking Prof. Jonathan Kelley for making good illustration. I felt it could be better discussing more about sizes of covariance matrices, so that it would help in better understanding of the algebra. Overall a good taught and informative course. Thank you Coursera.

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

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.

Quite a mathematically extensive course, but how the instructors teach will clear all your doubts! The concepts taught apply not only to Self Driving Cars but for any general system. All in all, an excellent course for State Estimation.

The programming assignments given tested us on how well we understood the fundamentals of localization. The solutions were not trivial and one had to think while programming which speaks to how well these assignments were designed