The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, ...
A Lidar-Inertial State Estimator for Robust and Efficient Navigation based on iterated error-state Kalman filter - ChaoqinRobotics/LINS---LiDAR-inertial-SLAM
This process is iterated N times to produce N sets of “Observation yt” and “R matrix” with exact corresponding relationships. In this study, T is set to 20 h, and the fireline state Xti at each time step is composed of a set of k = 100 fire points, i.e., Xti = [...
This feature likely represented the frequency with which the algorithm iterated to refine estimated locations, suggesting that increasing the number of iterations could lead to enhanced localization accuracy, albeit potentially at the expense of computational complexity. Additionally, “node density” got a...
The field state[v]∈{0,1,2} indicates if a path from the root vr to v has been discovered (state[v]⩾1) and if v has already been processed (state[v]=2). • The field weight[v] contains the weight of the heaviest discovered walk from vr to v. • The field parent[v] ...
thus providing a recursive solution, the Kalman filter, for the least-squares approach first used by C. F. Gauss (1777–1855) in planetary orbit estimation. The Kalman filter is the natural extension of the Wiener filter to non-stationary stochastic systems. See, for example, “Applied Optimal...
In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to a...
the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance.This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter(ACKF).The Kalman filter method inherently provides state error...
the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance.This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter(ACKF).The Kalman filter method inherently provides s...
The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, ...