ベイズニューラルネットワークによる乗法的相互作用学習を用いた視覚慣性オドメトリ
Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
マルチヘッド自己注意でセンサ間の乗法的相互作用を学習し、ラプラス近似で不確実性を考慮した単眼視覚慣性オドメトリ手法を提案。KITTIで最先端手法を上回り、センサ故障への頑健性を示した。
著者: Kashmira Shinde, Jongseok Lee, Matthias Humt, Aydin Sezgin, Rudolph Triebel
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
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.
