{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-incremental-state-estimation-through","title":"Robust Incremental State Estimation through Covariance Adaptation","arxiv_id":"1910.05382","date":"2019-10-11","proceeding":null,"authors":[],"abstract":"Recent advances in the fields of robotics and automation have spurred\nsignificant interest in robust state estimation. To enable robust state\nestimation, several methodologies have been proposed. One such technique, which\nhas shown promising performance, is the concept of iteratively estimating a\nGaussian Mixture Model (GMM), based upon the state estimation residuals, to\ncharacterize the measurement uncertainty model. Through this iterative process,\nthe measurement uncertainty model is more accurately characterized, which\nenables robust state estimation through the appropriate de-weighting of\nerroneous observations. This approach, however, has traditionally required a\nbatch estimation framework to enable the estimation of the measurement\nuncertainty model, which is not advantageous to robotic applications. In this\npaper, we propose an efficient, incremental extension to the measurement\nuncertainty model estimation paradigm. The incremental covariance estimation\n(ICE) approach, as detailed within this paper, is evaluated on several\ncollected data sets, where it is shown to provide a significant increase in\nlocalization accuracy when compared to other state-of-the-art robust,\nincremental estimation algorithms.","url_abs":"http://arxiv.org/abs/1910.05382v1","url_pdf":"http://arxiv.org/pdf/1910.05382v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-incremental-state-estimation-through","repo_url":"https://github.com/wvu-navLab/ICE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"state-estimation","task_name":"State Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}