{"id":3220,"date":"2025-06-22T13:32:33","date_gmt":"2025-06-22T03:32:33","guid":{"rendered":"https:\/\/braininspirednavigation.com\/?p=3220"},"modified":"2025-06-22T13:32:33","modified_gmt":"2025-06-22T03:32:33","slug":"how-to-build-a-fully-neuromorphic-localization-system-capable-of-large-scale-on-device-deployment-for-energy-efficient-robotic-place-recognition","status":"publish","type":"post","link":"https:\/\/braininspirednavigation.com\/?p=3220","title":{"rendered":"How to build a fully neuromorphic localization system capable of large-scale, on-device deployment for energy-efficient robotic place recognition?"},"content":{"rendered":"<p style=\"text-align: justify;\">Adam D. Hines, Michael Milford, Tobias Fischer. <a href=\"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.ads3968\"><strong>A compact neuromorphic system for ultra\u2013energy-efficient, on-device robot localization<\/strong><\/a>. Sci. Robot.10, eads3968(2025).DOI:10.1126\/scirobotics.ads3968<\/p>\n<p style=\"text-align: justify;\">Abstract<br \/>\n&#8220;<strong><span style=\"color: #ff0000;\">Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge<\/span><\/strong>. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms, which still require accurate long-endurance localization. <strong><span style=\"color: #ff0000;\">Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of biorealistic networks<\/span><\/strong>. To overcome this challenge, fusion of hardware and algorithms is critical when using this specialized computing paradigm. Here, <strong><span style=\"color: #ff0000;\">we demonstrate a neuromorphic localization system that performs competitive place recognition in up to 8 kilometers of traversal using models as small as 180 kilobytes with 44,000 parameters while consuming less than 8% of the energy required by conventional methods<\/span><\/strong>. Our system, locational encoding with neuromorphic systems (LENS), integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SynSense Speck chip, enabling real-time, energy-efficient localization on a hexapod robot. When compared with a benchmark place recognition method, sum of absolute differences, LENS performs comparably in overall precision. <strong><span style=\"color: #ff0000;\">LENS represents an accurate fully neuromorphic localization system capable of large-scale, on-device deployment for energy-efficient robotic place recognition<\/span><\/strong>. Neuromorphic computing enables resource-constrained robots to <strong><span style=\"color: #ff0000;\">perform energy-efficient, accurate localization<\/span><\/strong>.&#8221;<\/p>\n<p style=\"text-align: justify;\">Adam D. Hines, Michael Milford, Tobias Fischer. <a href=\"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.ads3968\"><strong>A compact neuromorphic system for ultra\u2013energy-efficient, on-device robot localization<\/strong><\/a>. Sci. Robot.10, eads3968(2025).DOI:10.1126\/scirobotics.ads3968<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Adam D. Hines, Michael Milford, Tobias Fischer. A compact neuromorphic system for ultra\u2013energy-efficient, on-device robot localization. Sci. Robot.10, eads3968(2025).DOI:10.1126\/scirobotics.ads3968 Abstract &#8220;Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1468,114],"tags":[1469,603,1471,1470,1472],"_links":{"self":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3220"}],"collection":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3220"}],"version-history":[{"count":1,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3220\/revisions"}],"predecessor-version":[{"id":3221,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/3220\/revisions\/3221"}],"wp:attachment":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}