{"id":22636477,"url":"https://github.com/mrdvince/slam","last_synced_at":"2026-05-09T05:31:50.214Z","repository":{"id":106602820,"uuid":"335870452","full_name":"mrdvince/slam","owner":"mrdvince","description":"Implement SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world. 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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. \n\n*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.*\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./images/robot_world.png\" width=50% height=50% /\u003e\n\u003c/p\u003e\n\n\n### Local Environment Instructions\n\n1. Clone the repository, and navigate to the downloaded folder.\n```\ngit clone https://github.com/mrdvince/slam.git\ncd slam\n```\n\n2. 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.\n\n\t- __Linux__ or __Mac__: \n\t```\n\tconda create -n slam python=3.6\n\tsource activate slam\n\t```\n\t- __Windows__: \n\t```\n\tconda create --name slam python=3.6\n\tactivate slam\n\t```\n\t\n\tAt this point your command line should look something like: `(slam) \u003cUser\u003e:slam \u003cuser\u003e$`. The `(slam)` indicates that your environment has been activated, and you can proceed with further package installations.\n\n6. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).\n```\npip install -r requirements.txt\n```\n\n\n## Notebooks\n\n1. Navigate back to the repo. (Also, your source environment should still be activated at this point.)\n```shell\ncd\ncd slam\n```\n\n2. 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.\n```shell\njupyter notebook\n```\n\n3. Once you open any of the project notebooks, make sure you are in the correct `slam` environment by clicking `Kernel \u003e Change Kernel \u003e slam`.\n\nLICENSE: This project is licensed under the terms of the MIT license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrdvince%2Fslam","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmrdvince%2Fslam","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrdvince%2Fslam/lists"}