Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation

The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.

Section: Proposed Methodsmentioning

confidence: 99%

Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy

Alteriis

Accardo

Conte

et al. 2021

“…However, this system is vulnerable to error accumulation over time, and it is not able to self-calibrate the accumulative error. As a countermeasure, internal and external sensors are integrated for the estimation of the robot’s attitude and position [ 12 , 13 , 14 ]. In these systems, the accurate attitude and position information are usually obtained by the sensors mounted inside the object (internal sensors), and external sensors in the environment are used to further calibrate this information.…”

Section: Introductionmentioning

confidence: 99%

An IMU/ODM/UWB Joint Localization System Based on Modified Cubature Kalman Filtering

Tang

Dou

2021

In this article, a multisensor joint localization system is proposed based on modified cubature Kalman filtering, which aims to improve the accuracy of state estimation under a moderate computational burden in the presence of high process noise. Specifically, first, the covariance of process noise is matched based on adaptive filtering. The inertial measurement unit (IMU), odometer (ODM), and ultra-wideband (UWB) information acquired by the associated sensors is then employed to augment the system state and are fused to lower the influence of process noise. In the presented localization setting, all sensors (IMU/ODM/UWB) are set to work in parallel under the federated Kalman filter (FKF) framework, which can correct the cumulative error of the internal sensor and and can improve the computational efficiency. Two sets of numerical simulations were performed to show that the proposed method can obtain accurate state estimation with a slightly increased computational burden.

“…A unit combining a MARG sensor with the electronics running the estimation algorithm is commonly called Attitude and Heading Reference System (AHRS). In modern outdoor applications, they are often combined with GNSS measurements, as in [4]. Most AHRSs have severe constraints concerning their size, weight and energy consumption and have limited computational power [5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Kalman Filter for Nonlinear Attitude Estimation Using Time Variable Matrices and Quaternions

Deibe

Nacimiento

Cardenal

et al. 2020

The nonlinear problem of sensing the attitude of a solid body is solved by a novel implementation of the Kalman Filter. This implementation combines the use of quaternions to represent attitudes, time-varying matrices to model the dynamic behavior of the process and a particular state vector. This vector was explicitly created from measurable physical quantities, which can be estimated from the filter input and output. The specifically designed arrangement of these three elements and the way they are combined allow the proposed attitude estimator to be formulated following a classical Kalman Filter approach. The result is a novel estimator that preserves the simplicity of the original Kalman formulation and avoids the explicit calculation of Jacobian matrices in each iteration or the evaluation of augmented state vectors.