1
00:00:03,650 --> 00:00:08,600
This course is structured in such a way that each of the topics builds on the previous topics to build

2
00:00:08,600 --> 00:00:14,150
up until we get to the final learning outcomes we have covered in the past that the assumed knowledge

3
00:00:14,150 --> 00:00:17,560
for this course is basically probability and uncertainty.

4
00:00:17,990 --> 00:00:25,070
This squares estimation, linear dynamic systems and C++ programming to all these different areas are

5
00:00:25,070 --> 00:00:26,870
assumed knowledge for this course.

6
00:00:27,410 --> 00:00:32,660
And we'll use each of these areas and build upon each of them to build up our different skill sets.

7
00:00:33,830 --> 00:00:36,690
So first, we introduced the linear common filter.

8
00:00:37,010 --> 00:00:42,170
So this takes concepts from probability and uncertainty, least squares estimation and linear dynamic

9
00:00:42,180 --> 00:00:45,650
systems and builds them up into the linear carbon filter.

10
00:00:46,280 --> 00:00:49,030
We'll also look at the implementation of this filter.

11
00:00:49,040 --> 00:00:54,770
So we use our C++ previous knowledge and look at the linear common implementation problem.

12
00:00:55,510 --> 00:01:00,140
Once you've looked at the linear current filter, you then take the concepts and extend them forward

13
00:01:00,140 --> 00:01:03,280
into nonlinear systems using the extended common filter.

14
00:01:03,770 --> 00:01:08,410
And we do the same thing for the implementation was we looked at the extended common filter.

15
00:01:08,450 --> 00:01:12,830
We'll look at the unscented come a filter and the unscented come filter implementation.

16
00:01:15,090 --> 00:01:19,740
Once we've covered the three different types of common foods as shown here, we'll then look at some

17
00:01:19,740 --> 00:01:23,680
additional topics, so we'll look at how to do filtering in the real world.

18
00:01:23,720 --> 00:01:29,150
So what sort of common errors and situations do we have to compensate for in the real world?

19
00:01:30,450 --> 00:01:35,790
Finally, after we've learned all that, will then look at the capstone project, which is taking concepts

20
00:01:35,790 --> 00:01:42,720
about filtering in the real world and generating a common filter based on the final U.K. implementation

21
00:01:42,990 --> 00:01:45,130
to run for the capstone project.

22
00:01:45,360 --> 00:01:51,240
So this capstone project is to implement a filter for a self-driving car, assuming real world data

23
00:01:51,240 --> 00:01:52,050
for you and problems.