XIRVIO: 説明可能な適応的重み付けを備えたクリティック誘導型反復改良による視覚慣性オドメトリ
XIRVIO: Critic-guided Iterative Refinement for Visual-Inertial Odometry with Explainable Adaptive Weighting
画像と慣性計測を入力とし、TransformerベースのGANで姿勢軌道を反復的に改良して予測する視覚慣性オドメトリ手法を提案し、センサ重み付けの説明可能性を示した。
著者: Chit Yuen Lam, Ronald Clark, Basaran Bahadir Kocer
分類: cs.RO
原文アブストラクト
We introduce XIRVIO, a transformer-based Generative Adversarial Network (GAN) framework for monocular visual inertial odometry (VIO). By taking sequences of images and 6-DoF inertial measurements as inputs, XIRVIO's generator predicts pose trajectories through an iterative refinement process which are then evaluated by the critic to select the iteration with the optimised prediction. Additionally, the self-emergent adaptive sensor weighting reveals how XIRVIO attends to each sensory input based on contextual cues in the data, making it a promising approach for achieving explainability in safety-critical VIO applications. Evaluations on the KITTI dataset demonstrate that XIRVIO matches well-known state-of-the-art learning-based methods in terms of both translation and rotation errors.
