Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the unscented Kalman filter (UKF) and the particle filter (PF) were compared for the case of significant plant-model mismatch, and the PF was shown to be less robust than the Kalman update-based filters.
37 citations
TL;DR: In this paper, an unscented Kalman filter (UKF) is employed to determine the biases associated to each accelerometer and gyro in the inertial measurement unit (IMU), together with high sampling-rate trajectory reconstruction from low frequency sampled GPS data.
37 citations
TL;DR: This paper presents, for the first time, a unified, explicit and restriction free set of design formulas for Kalman type and Luenberger type state observers and function observers, with arbitrary poles, for recovering the robustness of a direct state feedback system.
37 citations
03 Jun 2015
TL;DR: This work compares two modern approaches to ego motion estimation: the Multi-State Constraint Kalman Filter (MSCKF) and the Sliding Window Filter (SWF), which is computationally cheaper, has good consistency properties, and improves in accuracy as more features are tracked.
Abstract: Accurate and consistent ego motion estimation is a critical component of autonomous navigation. For this task, the combination of visual and inertial sensors is an inexpensive, compact, and complementary hardware suite that can be used on many types of vehicles. In this work, we compare two modern approaches to ego motion estimation: the Multi-State Constraint Kalman Filter (MSCKF) and the Sliding Window Filter (SWF). Both filters use an Inertial Measurement Unit (IMU) to estimate the motion of a vehicle and then correct this estimate with observations of salient features from a monocular camera. While the SWF estimates feature positions as part of the filter state itself, the MSCKF optimizes feature positions in a separate procedure without including them in the filter state. We present experimental characterizations and comparisons of the MSCKF and SWF on data from a moving hand-held sensor rig, as well as several traverses from the KITTI dataset. In particular, we compare the accuracy and consistency of the two filters, and analyze the effect of feature track length and feature density on the performance of each filter. In general, our results show the SWF to be more accurate and less sensitive to tuning parameters than the MSCKF. However, the MSCKF is computationally cheaper, has good consistency properties, and improves in accuracy as more features are tracked.
37 citations
TL;DR: Constructive procedures for the design of such a linear functional observer are deduced from the existence conditions in the case where the order of the observer is equal to the number of observed variables.
Abstract: The technical note deals with existence conditions of a functional observer for linear time-varying systems in the case where the order of the observer is equal to the number of observed variables. Constructive procedures for the design of such a linear functional observer are deduced from the existence conditions. As a specific feature, the proposed procedures do not require the solution of a differential Sylvester equation. Some examples illustrate the presented results.
37 citations