効率的なSchmidt-EKFによる3D視覚慣性SLAM
An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM
IMUと単眼カメラを密結合し、Schmidtカルマンフィルタで成熟した特徴点をナッジパラメータとして扱うことで計算量を地図サイズに対して線形に抑えた高精度3D視覚慣性SLAMを提案。
著者: Patrick Geneva, James Maley, Guoquan Huang
分類: cs.CV, cs.RO
原文アブストラクト
It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU measurements and monocular images in a tightly-coupled manner to provide 3D motion tracking with bounded error. In particular, we adapt the Schmidt Kalman filter formulation to selectively include informative features in the state vector while treating them as nuisance parameters (or Schmidt states) once they become matured. This change in modeling allows for significant computational savings by no longer needing to constantly update the Schmidt states (or their covariance), while still allowing the EKF to correctly account for their cross-correlations with the active states. As a result, we achieve linear computational complexity in terms of map size, instead of quadratic as in the standard SLAM systems. In order to fully exploit the map information to bound navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature matching to find reliable correspondences between current 2D visual measurements and 3D map features. The proposed SEVIS is extensively validated in both simulations and experiments.
