Shinde, Kashmira und Lee, Jongseok und Humt, Matthias und Sezgin, Aydin und Triebel, Rudolph (2020) Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry. In: Workshop on AI for Autonomous Driving (AIAD), the 37th International Conference on Machine Learning (ICML). Workshop on AI for Autonomous Driving (AIAD), the 37 th International Conference on Machine Learning (ICML), 2020-07-13, Vienna, Austria.
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Kurzfassung
This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios. The proposed network makes use of a multi-head self-attention mechanism that learns multiplicative interactions between multiple streams of information. Another design feature of our approach is the incorporation of the model uncertainty using scalable Laplace Approximation. We evaluate the performance of the proposed approach by comparing it against the end-to-end state-of-the-art methods on the KITTI dataset and show that it achieves superior performance. Importantly, our work thereby provides an empirical evidence that learning multiplicative interactions can result in a powerful inductive bias for increased robustness to sensor failures.
elib-URL des Eintrags: | https://elib.dlr.de/135547/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Anderer) | ||||||||||||||||||||||||
Titel: | Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry | ||||||||||||||||||||||||
Autoren: |
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Datum: | 13 Juli 2020 | ||||||||||||||||||||||||
Erschienen in: | Workshop on AI for Autonomous Driving (AIAD), the 37th International Conference on Machine Learning (ICML) | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Multimodal learning, Autonomous Driving, Visual-Inertial Odometry, Robot Perception, Machine Learning, Deep Learning | ||||||||||||||||||||||||
Veranstaltungstitel: | Workshop on AI for Autonomous Driving (AIAD), the 37 th International Conference on Machine Learning (ICML) | ||||||||||||||||||||||||
Veranstaltungsort: | Vienna, Austria | ||||||||||||||||||||||||
Veranstaltungsart: | Workshop | ||||||||||||||||||||||||
Veranstaltungsdatum: | 13 Juli 2020 | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Technik für Raumfahrtsysteme | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R SY - Technik für Raumfahrtsysteme | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Intelligente Mobilität (alt), Vorhaben Intelligente Mobilität (alt) | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition | ||||||||||||||||||||||||
Hinterlegt von: | Lee, Jongseok | ||||||||||||||||||||||||
Hinterlegt am: | 21 Jul 2020 09:46 | ||||||||||||||||||||||||
Letzte Änderung: | 15 Okt 2024 08:51 |
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