{"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/event-based-camera-pose-tracking-using-a","title":"Event-based Camera Pose Tracking using a Generative Event Model","arxiv_id":"1510.01972","date":"2015-10-07","proceeding":null,"authors":["Guillermo Gallego","Christian Forster","Elias Mueggler","Davide Scaramuzza"],"abstract":"Event-based vision sensors mimic the operation of biological retina and they\nrepresent a major paradigm shift from traditional cameras. Instead of providing\nframes of intensity measurements synchronously, at artificially chosen rates,\nevent-based cameras provide information on brightness changes asynchronously,\nwhen they occur. Such non-redundant pieces of information are called \"events\".\nThese sensors overcome some of the limitations of traditional cameras (response\ntime, bandwidth and dynamic range) but require new methods to deal with the\ndata they output. We tackle the problem of event-based camera localization in a\nknown environment, without additional sensing, using a probabilistic generative\nevent model in a Bayesian filtering framework. Our main contribution is the\ndesign of the likelihood function used in the filter to process the observed\nevents. Based on the physical characteristics of the sensor and on empirical\nevidence of the Gaussian-like distribution of spiked events with respect to the\nbrightness change, we propose to use the contrast residual as a measure of how\nwell the estimated pose of the event-based camera and the environment explain\nthe observed events. The filter allows for localization in the general case of\nsix degrees-of-freedom motions.","url_abs":"http://arxiv.org/abs/1510.01972v1","url_pdf":"http://arxiv.org/pdf/1510.01972v1.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":"event-based-camera-pose-tracking-using-a","repo_url":"https://github.com/nkdnnlr/Event-Based-Camera-Simultaneous-Mosaicing-and-Tracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"event-based-camera-pose-tracking-using-a","repo_url":"https://github.com/uzh-rpg/rpg_image_reconstruction_from_events","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"camera-localization","task_name":"Camera Localization"},{"task_slug":"event-based-vision","task_name":"Event-based vision"},{"task_slug":"pose-tracking","task_name":"Pose Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1510.01972","atlas_url":"https://app.syntology.ai/?focus=1510.01972","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}