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.

the main missing point in this specialization is ROS. it is widely used and is the industry standard in autonomous cars and robotics and this course can be updated based on ROS related topics; integrating everything in the specialization in ROS.

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

Excellent course. You go from learning the basic concept of state estimation and localization all the way to solving a realistic state estimation problem. The course is quite dynamic, mixing theoretical concepts with real implementation.

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

Complex, informative and thoughtful course. It can be seen that the authors spent a lot of time preparing it. For me, it was one big puzzle, which was a pleasure to pass. Thanks to the Authors for their work!

One of the best courses I had on Coursera. Some modules are apparently easy and fast, but the whole course should be well understood in order to pass the final assignment. I had to go back and forth for th

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.

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 course is informative and well constructed for learners. The final project is designed well so that we can build sensor fusion tools while applying what we have learned from this course.

This is an eye-opening course on how to utilize statistical analysis for engineering applications, and in particular, autonomous systems, as such it is very useful and captivating course!!!

The course is highly informative and offers excellent opportunities to gain practical, hands-on skills essential for real-world autonomous vehicle applications.

Very good to learn Kalman Filter, and Extened Kalman Filter, espcially the good explanation on why it is effective, and restriction (when it is noise, etc).

Video lectures arer great. Programming assignments are also well designed. I just hoped more info of how input data for the last assignment was acquired.

A great Journey for anyone interested in Self Driving Cars. State estimation is vital in this field and this is a great course to start learning it!

Well Planned course. Giving introduction level details to domain State estimation and localization. Very great detail of Kalman Filter available.

Really interesting content and test. Definitely there are lots of advance concepts, so you will need to dedicate quite a lot of time to success.

Very challenging, nevertheless excelent for learning automation concepts, python programming, sensor fusion, probability & statistics

I have learned KF in the past. First time learning EKF. I liked the rigor in this course! Felt like a legitimate university lesson.

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