This repository contains project files for Computer Vision. It combine knowledge of robot sensor measurements and movement to create a map of an environment over time.
There is a large variety of SLAM (Simultaneous Localization and Mapping) approaches available in the robotics community. Throughout this work we focus on graph-based SLAM approach, a robust method for tracking an object over time and mapping out its surronding environment, using elements of probability, motion models and linear algebra.
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.
The project is structured as a series of Jupyter notebooks that are written in Python and designed to be completed in sequential order:
Notebook 1 : Robot Moving and Sensing;
Notebook 2 : Omega and Xi, Constraints;
Notebook 3 : Landmark Detection and Tracking.
$ git clone https://github.com/nalbert9/Landmark_Detection_Tracking.git
$ sudo pip3 install -r requirements.txtRobot:
| True last pose | x=24.09 y=62.98 |
|---|---|
| Predicted last pose | x=23.45 y=64.13 |
Landmarks:
| True Landmark | [[22, 91], [3, 58], [76, 52], [73, 80], [49, 6]] |
|---|---|
| Estimated Landmarks | [[21.8, 90.9], [3.1, 58.2], [76.2, 52.3], [73.4, 80.4], [49.1, 6.2]] |
This project is licensed under the terms of the
