{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:34:43Z","timestamp":1792290883247,"version":"4.4.0"},"reference-count":25,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T00:00:00Z","timestamp":1567209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["425401\/2018-9"],"award-info":[{"award-number":["425401\/2018-9"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP18H04125"],"award-info":[{"award-number":["JP18H04125"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an end-to-end learning framework for performing 6-DOF odometry by using only inertial data obtained from a low-cost IMU. The proposed inertial odometry method allows leveraging inertial sensors that are widely available on mobile platforms for estimating their 3D trajectories. For this purpose, neural networks based on convolutional layers combined with a two-layer stacked bidirectional LSTM are explored from the following three aspects. First, two 6-DOF relative pose representations are investigated: one based on a vector in the spherical coordinate system, and the other based on both a translation vector and an unit quaternion. Second, the loss function in the network is designed with the combination of several 6-DOF pose distance metrics: mean squared error, translation mean absolute error, quaternion multiplicative error and quaternion inner product. Third, a multi-task learning framework is integrated to automatically balance the weights of multiple metrics. In the evaluation, qualitative and quantitative analyses were conducted with publicly-available inertial odometry datasets. The best combination of the relative pose representation and the loss function was the translation and quaternion together with the translation mean absolute error and quaternion multiplicative error, which obtained more accurate results with respect to state-of-the-art inertial odometry techniques.<\/jats:p>","DOI":"10.3390\/s19173777","type":"journal-article","created":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T03:16:12Z","timestamp":1567394172000},"page":"3777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["End-to-End Learning Framework for IMU-Based 6-DOF Odometry"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1834-5221","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Silva do Monte Lima","sequence":"first","affiliation":[{"name":"Departamento de Computa\u00e7\u00e3o, Universidade Federal Rural de Pernambuco, Recife 52171-900, Brazil"},{"name":"Voxar Labs, Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6119-1184","authenticated-orcid":false,"given":"Hideaki","family":"Uchiyama","sequence":"additional","affiliation":[{"name":"Library, Kyushu University, Fukuoka 819-0395, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rin-ichiro","family":"Taniguchi","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Delmerico, J., and Scaramuzza, D. 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