A Versatile Keyframe-Based Structureless Filter for Visual Inertial Odometry
This work provides a versatile and efficient VIO solution for robotics applications, particularly beneficial for systems using consumer-grade sensors that require online calibration and consistent motion estimation.
This paper introduces a Keyframe-based Structureless Filter (KSF) for Visual Inertial Odometry (VIO) that efficiently estimates device motion with consistent covariance and performs online sensor calibration. KSF achieves real-time performance, running at 42 Hz with stereo camera images on a consumer laptop, while rivaling the accuracy of recent VIO methods.
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.