# Graph-based_SLAM

**Repository Path**: sunyiboshi_admin/Graph-based_SLAM

## Basic Information

- **Project Name**: Graph-based_SLAM
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-16
- **Last Updated**: 2024-10-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# **Graph-based SLAM (2D)** #

This repository contains conversion of [airlab summer school 2020 gtsam/slam](https://theairlab.org/summer2020/#2.3) matlab scripts to the python scripts.
For more detailed theoretical information, you can read ["A Tutorial on Graph-Based SLAM"](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf) article 
and take a look at ["Mobile Sensing and Robotics 2"](https://youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) -
[Graph-based SLAM using Pose Graphs](https://youtu.be/uHbRKvD8TWg) and [Graph-Based SLAM with Landmarks](https://youtu.be/mZBdPgBtrCM) lectures given by Cyrill Stachniss at the University of Bonn.


Graph-based SLAM : Build the graph and find a node configuration that minimize the error introduced by the constraints.


<img src="graph-slam-picture.png" width="600" />

$x_n =$ robot poses

$u_n =$ odometry measurements 

$m_n =$ landmark measurements 

$l_n =$ landmark positions 

Detailed information is available in (SLAM.ipynb).

Update: Added conversion of GTSAM c++ code to python code. (GTSAM.ipynb)