LBDU-VIO: 視覚信頼性が低い環境向けの学習型バイアス動態と不確かさを統合した視覚慣性オドメトリ
LBDU-VIO: Learned Bias Dynamics and Uncertainty for Visual-Inertial Odometry with Unreliable Vision
視覚情報が不安定な場面でも頑健に動くよう、IMUバイアスの連続時間動態と運動適応的なノイズ共分散をニューラルODEで学習し、MSCKFに組み込んだ視覚慣性オドメトリ手法。
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著者: Qizhi Guo, Junning Lyu, Defu Lin, Shaoming He
分類: cs.RO
原文アブストラクト
Visual-inertial odometry (VIO) for aerial robots relies on high rate inertial measurement unit (IMU) propagation between visual updates. However, conventional multi state constraint Kalman filters (MSCKFs) use random walk bias assumptions and fixed noise parameters, which can limit robustness when visual information is unreliable. To address this problem, we propose LBDU-VIO, a learning-augmented MSCKF with learned continuous time bias dynamics and an IMU uncertainty model. A neural ordinary differential equation (ODE) models continuous time bias dynamics to propagate the filter's bias states, replacing their random walk model. The IMU uncertainty model predicts motion adaptive measurement noise covariances for covariance propagation. Both models are trained with pose supervision without direct labels. Experiments on real world EuRoC and TUM-VI benchmarks show lower errors than representative visual-inertial baselines, including a 25.1% reduction in mean relative position error compared with S-MSCKF on EuRoC sequences with 10s visual outage.
