VIO-DualProNet: 学習ベースのプロセスノイズ共分散推定を用いた視覚慣性オドメトリ
VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariance
慣性センサのノイズ不確かさを深層学習でリアルタイム推定し、VINS-Monoに統合することで視覚慣性オドメトリの精度とロバスト性を向上させた研究。
著者: Dan Solodar, Itzik Klein
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Visual-inertial odometry (VIO) is a vital technique used in robotics, augmented reality, and autonomous vehicles. It combines visual and inertial measurements to accurately estimate position and orientation. Existing VIO methods assume a fixed noise covariance for the inertial uncertainty. However, accurately determining in real-time the noise variance of the inertial sensors presents a significant challenge as the uncertainty changes throughout the operation leading to suboptimal performance and reduced accuracy. To circumvent this, we propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial noise uncertainty in real-time. By designing and training a deep neural network to predict inertial noise uncertainty using only inertial sensor measurements, and integrating it into the VINS-Mono algorithm, we demonstrate a substantial improvement in accuracy and robustness, enhancing VIO performance and potentially benefiting other VIO-based systems for precise localization and mapping across diverse conditions.
