Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation

Abstract

In this paper measurements from a monocular vision system are fused with inertial/magnetic measurements from an Inertial Measurement Unit (IMU) rigidly connected to the camera. Two Extended Kalman filters (EKFs) were developed to estimate the pose of the IMU/camera sensor moving relative to a rigid scene (ego-motion), based on a set of fiducials. The two filters were identical as for the state equation and the measurement equations of the inertial/magnetic sensors. The DLT-based EKF exploited visual estimates of the ego-motion using a variant of the Direct Linear Transformation (DLT) method; the error-driven EKF exploited pseudo-measurements based on the projection errors from measured two-dimensional point features to the corresponding three-dimensional fiducials. The two filters were off-line analyzed in different experimental conditions and compared to a purely IMU-based EKF used for estimating the orientation of the IMU/camera sensor. The DLT-based EKF was more accurate than the error-driven EKF, less robust against loss of visual features, and equivalent in terms of computational complexity. Orientation root mean square errors (RMSEs) of 1° (1.5°), and position RMSEs of 3.5 mm (10 mm) were achieved in our experiments by the DLT-based EKF (error-driven EKF); by contrast, orientation RMSEs of 1.6° were achieved by the purely IMU-based EKF.

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

“…Many researchers face this problem using Extended Kalman Filters [15], [16], [17]. For instance, Rehbinder and Hu have previously designed an algorithm [18] for fusing inclinometer and gyro data assuming low translational accelerations, which may not be very realistic for a walking robot.…”

Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment

“…차량의 움직임을 예측하는 방법에는 영상을 세분화 하여 예측하는 방법 [1,2] 과 RANSAC(RANdom SAmple Consensus)을 이용하여 예측하는 방법 [3] 그 리고 칼만 필터를 이용하여 예측하는 방법 [4,5] 칼만 필터(Kalman filter) [7] [8,9] . …”

Motion Field Estimation Using U-Disparity Map in Vehicle Environment

“…Three methods can be used to estimate the vehicle's movement; a method of segmenting image, the RANSAC (RANdom SAmple Consensus) and the Kalman filter [1][2][3][4]. In the first method, the given image is segmented into three parts, an upper, a middle, and a lower and the upper and middle parts are used for estimation.…”