ファクターグラフを用いたロバストな視覚慣性オドメトリのための深層IMUバイアス推定
Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs
視覚追跡が失敗した際の視覚慣性オドメトリの誤差蓄積を抑えるため、IMUバイアスの進化をニューラルネットワークで学習し、ファクターグラフに統合する手法を提案。
著者: Russell Buchanan, Varun Agrawal, Marco Camurri, Frank Dellaert, Maurice Fallon
分類: cs.RO
原文アブストラクト
Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of additive Gaussian noise and a slowly changing bias which evolves as a random walk. In this work, we propose to train a neural network to learn the true bias evolution. We implement and compare two common sequential deep learning architectures: LSTMs and Transformers. Our approach follows from recent learning-based inertial estimators, but, instead of learning a motion model, we target IMU bias explicitly, which allows us to generalize to locomotion patterns unseen in training. We show that our proposed method improves state estimation in visually challenging situations across a wide range of motions by quadrupedal robots, walking humans, and drones. Our experiments show an average 15% reduction in drift rate, with much larger reductions when there is total vision failure. Importantly, we also demonstrate that models trained with one locomotion pattern (human walking) can be applied to another (quadruped robot trotting) without retraining.
