https://github.com/milosgajdos/go-estimate-examples
Examples of usage for go-estimate
https://github.com/milosgajdos/go-estimate-examples
estimation extended-kalman-filter filtering kalman-filter particle particle-filter unscented-kalman-filter
Last synced: 5 months ago
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Examples of usage for go-estimate
- Host: GitHub
- URL: https://github.com/milosgajdos/go-estimate-examples
- Owner: milosgajdos
- License: apache-2.0
- Created: 2019-10-10T23:48:16.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-20T11:05:13.000Z (over 2 years ago)
- Last Synced: 2025-04-06T10:02:43.992Z (6 months ago)
- Topics: estimation, extended-kalman-filter, filtering, kalman-filter, particle, particle-filter, unscented-kalman-filter
- Language: Go
- Size: 287 KB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# go-estimate-examples
[](https://godoc.org/github.com/milosgajdos/go-estimate-examples)
[](https://opensource.org/licenses/Apache-2.0)
This repo contains examples of usage for [go-estimate](https://github.com/milosgajdos/go-estimate)
First, you need to install [GoCV](https://gocv.io) as instructed [here](https://github.com/hybridgroup/gocv/#how-to-install).
You can build the examples by running the following command:
```shell
$ make examples
```
Get the repo:
```shell
go get github.com/milosgajdos/go-estimate-examples
```
Build examples:
```shell
make build
```
This will create a subdirectory in each of the examples directory called `_build` and place the built binaries into it. You can run the programs by executing any of the binaries.
Alternatively, you can also install the examples by either running `go install` for each of the examples or do it all with one command:
```shell
$ make install
```
Most of the examples are static i.e. they generate a simple plot which shows how the filter estimates new values from noisy measurements.
There are however [two](bfgocv) [examples](kfgocv) which use the wonderful [gocv](https://gocv.io/) library to generate short interactive videos to watch the filters live in action.
Example of bootstrap filter in action:
