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.

some equations weren't explained and remained ambiguous to me, needs more explanation on the mathematical side, other than that a great course and great effort

More help should be provided by better lectures and more explanation on the projects.

too hard, and the data is not good

good course

The coding part for each assignment should be explained in more detail

Poor explanation by the instructor