キーフレームベースの汎用構造なしフィルタによる視覚慣性オドメトリ
A Versatile Keyframe-Based Structureless Filter for Visual Inertial Odometry
ランドマークを状態ベクトルに含めずキーフレームで管理する構造なしフィルタを提案し、IMUやカメラのパラメータをオンライン校正しつつ高精度に自己位置推定するVIO手法を実現した。
著者: Jianzhu Huai, Yukai Lin, Charles Toth, Yuan Zhuang, Dong Chen
分類: cs.RO
原文アブストラクト
Motion estimation by fusing data from at least a camera and an Inertial Measurement Unit (IMU) enables many applications in robotics. However, among the multitude of Visual Inertial Odometry (VIO) methods, few efficiently estimate device motion with consistent covariance, and calibrate sensor parameters online for handling data from consumer sensors. This paper addresses the gap with a Keyframe-based Structureless Filter (KSF). For efficiency, landmarks are not included in the filter's state vector. For robustness, KSF associates feature observations and manages state variables using the concept of keyframes. For flexibility, KSF supports anytime calibration of IMU systematic errors, as well as extrinsic, intrinsic, and temporal parameters of each camera. Estimator consistency and observability of sensor parameters were analyzed by simulation. Sensitivity to design options, e.g., feature matching method and camera count was studied with the EuRoC benchmark. Sensor parameter estimation was evaluated on raw TUM VI sequences and smartphone data. Moreover, pose estimation accuracy was evaluated on EuRoC and TUM VI sequences versus recent VIO methods. These tests confirm that KSF reliably calibrates sensor parameters when the data contain adequate motion, and consistently estimate motion with accuracy rivaling recent VIO methods. Our implementation runs at 42 Hz with stereo camera images on a consumer laptop.
