{"id":18735302,"url":"https://github.com/josancamon19/landmark_detection_and_tracking","last_synced_at":"2026-01-24T07:32:43.568Z","repository":{"id":104742858,"uuid":"177190768","full_name":"josancamon19/landmark_detection_and_tracking","owner":"josancamon19","description":"Landmark detection and tracking project for Computer Vision Nanodegree.","archived":false,"fork":false,"pushed_at":"2019-03-30T21:27:09.000Z","size":689,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-01-17T03:25:31.506Z","etag":null,"topics":["computer-vision","computer-vision-nanodegree","deep-learning","udacity-nanodegree"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/josancamon19.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-03-22T18:27:17.000Z","updated_at":"2019-08-05T19:58:09.000Z","dependencies_parsed_at":null,"dependency_job_id":"f1094ad7-e88a-45e1-b327-f6383cf5fa44","html_url":"https://github.com/josancamon19/landmark_detection_and_tracking","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/josancamon19/landmark_detection_and_tracking","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/josancamon19%2Flandmark_detection_and_tracking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/josancamon19%2Flandmark_detection_and_tracking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/josancamon19%2Flandmark_detection_and_tracking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/josancamon19%2Flandmark_detection_and_tracking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/josancamon19","download_url":"https://codeload.github.com/josancamon19/landmark_detection_and_tracking/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/josancamon19%2Flandmark_detection_and_tracking/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28718960,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-24T05:53:42.649Z","status":"ssl_error","status_checked_at":"2026-01-24T05:53:41.698Z","response_time":89,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","computer-vision-nanodegree","deep-learning","udacity-nanodegree"],"created_at":"2024-11-07T15:16:23.360Z","updated_at":"2026-01-24T07:32:43.561Z","avatar_url":"https://github.com/josancamon19.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Udacity Computer Vision Nanodegree\n### Project Landmark detection and tracking\n\nIn this project, I implemented SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world! I combined\nwhat I learned about robot sensor measurements and movement to create a map of an environment from only sensor and \nmotion 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\nand identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active \narea of research in the fields of robotics and autonomous systems.\n\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 \n*only* sensor and motion data collected by that robot. This is just one example for a 50x50 grid world. \n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./images/robot_world.png\" width=50% height=50% /\u003e\n\u003c/p\u003e\n\nThe project was broken up into four Python notebooks; the first two are for exploration of provided code,\nand a review of SLAM architectures, **only Notebook 3 and the `robot_class.py` file contains the code built**:\n\n__Notebook 1__ : Robot Moving and Sensing\n\n__Notebook 2__ : Omega and Xi, Constraints \n\n__Notebook 3__ : Landmark Detection and Tracking \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjosancamon19%2Flandmark_detection_and_tracking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjosancamon19%2Flandmark_detection_and_tracking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjosancamon19%2Flandmark_detection_and_tracking/lists"}