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However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31\u00d7 to 134\u00d7 with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within \u00b10.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models.<\/jats:p>","DOI":"10.1145\/3534594","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T18:50:18Z","timestamp":1657219818000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["TinyOdom"],"prefix":"10.1145","volume":"6","author":[{"given":"Swapnil Sayan","family":"Saha","sequence":"first","affiliation":[{"name":"University of California - Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep Singh","family":"Sandha","sequence":"additional","affiliation":[{"name":"University of California - Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis Antonio","family":"Garcia","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mani","family":"Srivastava","sequence":"additional","affiliation":[{"name":"University of California - Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2895495"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2012.6214359"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40064-016-3573-7"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2017.05.006"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.37"},{"key":"e_1_2_2_6_1","volume-title":"Proceedings of Machine Learning and Systems 3","author":"Banbury Colby","year":"2021","unstructured":"Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, and Paul Whatmough. 2021. 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