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Unlike traditional methods that rely on deployment\u2010specific tuning or predefined motion priors, our approach generalises effectively across a wide range of real\u2010world scenarios, including autonomous vehicles, aerial drones, mobile robots and handheld devices. To this end, we introduce a mixture\u2010of\u2010experts strategy for local state estimation, with several specialised decoders that each handle a distinct class of ego\u2010motion patterns. Moreover, we introduce a fully differentiable Gumbel\u2010softmax module that constructs a robust inter\u2010frame correlation graph, selects the optimal expert decoder and prunes erroneous estimates. These cues are then fed into a unified back\u2010end that combines pretrained scale\u2010independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor\/autonomous driving), EuRoC\u2010MAV (indoor\/aerial drones) and TUM\u2010RGBD (indoor\/handheld), demonstrating state\u2010of\u2010the\u2010art performance.<\/jats:p>","DOI":"10.1049\/cit2.70095","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:00:47Z","timestamp":1766120447000},"page":"205-222","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["UNO: Unified Self\u2010Supervised Monocular Odometry for Platform\u2010Agnostic Deployment"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4125-239X","authenticated-orcid":false,"given":"Wentao","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Automation and Intelligent Sensing, Institute of Medical Robotics SJTU  Shanghai China"}]},{"given":"Yihe","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences Shanghai Jiao Tong University Shanghai Jiao Tong University  Shanghai China"}]},{"given":"Yanbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Intelligent Sensing, Institute of Medical Robotics SJTU  Shanghai China"}]},{"given":"Tianchen","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Automation and Intelligent Sensing, Institute of Medical Robotics SJTU  Shanghai China"}]},{"given":"Shenghai","family":"Yuan","sequence":"additional","affiliation":[{"name":"The Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University  Singapore Singapore"}]},{"given":"Zhenli","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Intelligence Technology CO. 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