{"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/jointly-optimizing-placement-and-inference","title":"Jointly Optimizing Placement and Inference for Beacon-based Localization","arxiv_id":"1703.08612","date":"2017-03-24","proceeding":null,"authors":["Charles Schaff","David Yunis","Ayan Chakrabarti","Matthew R. Walter"],"abstract":"The ability of robots to estimate their location is crucial for a wide\nvariety of autonomous operations. In settings where GPS is unavailable,\nmeasurements of transmissions from fixed beacons provide an effective means of\nestimating a robot's location as it navigates. The accuracy of such a\nbeacon-based localization system depends both on how beacons are distributed in\nthe environment, and how the robot's location is inferred based on noisy and\npotentially ambiguous measurements. We propose an approach for making these\ndesign decisions automatically and without expert supervision, by explicitly\nsearching for the placement and inference strategies that, together, are\noptimal for a given environment. Since this search is computationally\nexpensive, our approach encodes beacon placement as a differential neural layer\nthat interfaces with a neural network for inference. This formulation allows us\nto employ standard techniques for training neural networks to carry out the\njoint optimization. We evaluate this approach on a variety of environments and\nsettings, and find that it is able to discover designs that enable high\nlocalization accuracy.","url_abs":"http://arxiv.org/abs/1703.08612v2","url_pdf":"http://arxiv.org/pdf/1703.08612v2.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":"jointly-optimizing-placement-and-inference","repo_url":"https://github.com/cbschaff/nbp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}