{"id":31223691,"url":"https://github.com/mit-acl/tcaff","last_synced_at":"2025-09-21T22:54:41.189Z","repository":{"id":239663188,"uuid":"791332307","full_name":"mit-acl/tcaff","owner":"mit-acl","description":"A multiple hypothesis algorithm for aligning coordinate frames of collaborative robots","archived":false,"fork":false,"pushed_at":"2025-03-22T12:31:58.000Z","size":22928,"stargazers_count":10,"open_issues_count":1,"forks_count":0,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-03-22T13:32:10.432Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mit-acl.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2024-04-24T14:24:41.000Z","updated_at":"2025-03-22T12:32:02.000Z","dependencies_parsed_at":"2024-05-13T22:52:53.455Z","dependency_job_id":"7f8ba761-5476-4412-acf9-2c32cf854500","html_url":"https://github.com/mit-acl/tcaff","commit_stats":null,"previous_names":["mit-acl/motlee","mit-acl/tcaff"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mit-acl/tcaff","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mit-acl%2Ftcaff","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mit-acl%2Ftcaff/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mit-acl%2Ftcaff/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mit-acl%2Ftcaff/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mit-acl","download_url":"https://codeload.github.com/mit-acl/tcaff/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mit-acl%2Ftcaff/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276318990,"owners_count":25621651,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-21T02:00:07.055Z","response_time":72,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2025-09-21T22:54:37.603Z","updated_at":"2025-09-21T22:54:41.178Z","avatar_url":"https://github.com/mit-acl.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TCAFF: Temporal Consistency for Robot Frame Alignment\n\n![banner](./media/tcaff_overview.png)\n\nTCAFF (temporally consistent alignment of frames filter) is a novel multiple hypothesis algorithm for aligning the coordinate frames of neighboring robots.\nTCAFF considers potential alignments from associating sparse, open-set object maps and leverages temporal consistency to determine an initial alignment and correct for drift, all without any initial knowledge of neighboring robot poses.\n\nThis repo contains code for aligning frames using TCAFF as well as an example application of using TCAFF while performing collaborative multi-object tracking on a dataset of four robots tracking six pedestrians (see [below](#dataset) for access to our dataset).\n\n\u003cimg src=\"./media/experiment.gif\" alt=\"experiment\" width=\"800\"/\u003e\n\n# Citation\n\nIf you use this code in your research, please cite our paper:\n\nM.B. Peterson, P.C. Lusk, A. Avila and J.P. How, \"TCAFF: Temporal Consistency for Robot Frame Alignment,\" ICRA 2025.\n\n```\n@article{peterson2024tcaff,\n  title={TCAFF: Temporal Consistency for Robot Frame Alignment},\n  author={Peterson, Mason B and Lusk, Parker C and Avila, Antonio and How, Jonathan P},\n  journal={arXiv preprint arXiv:2405.05210},\n  year={2024}\n}\n```\n\n# Installation\n\nThe `tcaff` Python package can be installed with:\n\n```\ncd \u003cthis rep\u003e\npip install .\n```\n\nAdditionally, the steps for installing [CLIPPER](https://github.com/mit-acl/clipper) should be followed:\n\n```\ngit clone git@github.com:mit-acl/clipper.git\ncd clipper\nmkdir build\ncd build\ncmake ..\nmake\nmake pip-install\n```\n\n# Running\n\nAfter downloading the TCAFF MOT dataset, the demo can be run with the following commands (after `cd`ing into this repo):\n\n```\nmkdir results\npython3 ./demo/demo.py --params ./demo/params/tcaff_mot_dataset.yaml --output ./results/tcaff_mot_dataset.png\n```\nFor the code to recognize the dataset this environment variable should be set:\n```\nexport TCAFF_MOT_DATASET=\u003cpath to dataset\u003e\n```\n\n# Dataset\n\nThe TCAFF MOT dataset of four robots/six pedestrian object tracking experiment associated with the TCAFF paper can be obtained by filling out [this Google Form](https://forms.gle/aKoQqBDXJVYe38mK9).\n\n# Acknowledgements\n\nTCAFF was supported by the Ford Motor Company, ONR, and ARL DCIST.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-acl%2Ftcaff","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmit-acl%2Ftcaff","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-acl%2Ftcaff/lists"}