2020/12/30 by Jianzhu Huai, Yukai Lin, Huai, Jianzhu +7
Computer Science · Engineering · #Advanced Vision and Imaging #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2012.15170
openalex publication_date 2020/12/30 · openalex created_date 2021/01/05 · openalex updated_date 2026/07/28
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.