https://github.com/mrdvince/slam
Implement SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world. Combining robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time.
https://github.com/mrdvince/slam
computer-vision localization python robotics slam vision
Last synced: about 2 months ago
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Implement SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world. Combining robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time.
- Host: GitHub
- URL: https://github.com/mrdvince/slam
- Owner: mrdvince
- License: mit
- Created: 2021-02-04T07:12:50.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-01-18T10:42:46.000Z (over 4 years ago)
- Last Synced: 2025-10-30T07:45:02.554Z (8 months ago)
- Topics: computer-vision, localization, python, robotics, slam, vision
- Language: Jupyter Notebook
- Homepage:
- Size: 471 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Landmark Detection & Robot Tracking (SLAM)
## Project Overview
Implement SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world! You’ll combine what you know about robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active area of research in the fields of robotics and autonomous systems.
*Below is an example of a 2D robot world with landmarks (purple x's) and the robot (a red 'o') located and found using *only* sensor and motion data collected by that robot. This is just one example for a 50x50 grid world; in your work you will likely generate a variety of these maps.*
### Local Environment Instructions
1. Clone the repository, and navigate to the downloaded folder.
```
git clone https://github.com/mrdvince/slam.git
cd slam
```
2. Create (and activate) a new environment, named `cv-nd` with Python 3.6. If prompted to proceed with the install `(Proceed [y]/n)` type y.
- __Linux__ or __Mac__:
```
conda create -n slam python=3.6
source activate slam
```
- __Windows__:
```
conda create --name slam python=3.6
activate slam
```
At this point your command line should look something like: `(slam) :slam $`. The `(slam)` indicates that your environment has been activated, and you can proceed with further package installations.
6. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
```
pip install -r requirements.txt
```
## Notebooks
1. Navigate back to the repo. (Also, your source environment should still be activated at this point.)
```shell
cd
cd slam
```
2. Open the directory, using the below command. You'll see all of the files appear in your local environment; open the Landmark Detection and Tracking notebook and follow the instructions.
```shell
jupyter notebook
```
3. Once you open any of the project notebooks, make sure you are in the correct `slam` environment by clicking `Kernel > Change Kernel > slam`.
LICENSE: This project is licensed under the terms of the MIT license.