Model Validation of an Open-source Framework for Post-processing INS/GNSS Systems

The development of new approaches in the GIS research community may require the use of a computational tool to post-process GNSS and inertial sensors data in order to get more accurate position, velocity, and orientation angles (attitude) information. An open-source framework for simulating integrated navigation systems (INS/GNSS) called NaveGo has been developed using MATLAB/GNU Octave and is freely available on-line. Although preliminary tests have shown that NaveGo appears to work properly, a deep examination must be carried out to confirm that this framework is an adequate tool for post-processing INS/GNSS information. The main goal of this work is to produce a validation methodology to show that NaveGo mathematical model works within its specifications. Firstly, static measurements from inertial sensors are processed and analysed by NaveGo applying the Allan variance for profiling typical errors. Some details of Allan variance procedure are exhibited. Then, performances of NaveGo and Inertial Explorer, a closed-source commercial package software for INS/GNSS integration, are compared for a real-world trajectory. It is statistically concluded that NaveGo presents close accuracy to Inertial Explorer for attitude and position. Consequently, it is demonstrated that NaveGo is an useful INS/GNSS post-processing framework that can be used in GIS applications

Identificación y compensación del sesgo en sensores inerciales MEMS de muy bajo costo mediante el uso de algoritmos de aprendizaje automático

En los últimos años, el uso de sensores inerciales basados en tecnología MEMS (MicroElectroMechanical Systems) se ha consolidado en diferentes áreas que requieren sensores baratos, livianos y de bajo consumo de energía. Algunas de estas áreas son la robótica móvil aérea y la robótica móvil terrestre. Una desventaja que presentan este tipo de sensores es su bajo desempeño comparado con sensores fabricados con otras tecnologías. Los dos errores más gravitantes en el desempeño de un sensor inercial MEMS son el ruido blanco y el sesgo presentes en la señal de salida del sensor. En particular, el sesgo presenta una variación no lineal muy notoria respecto a la temperatura, debido a que los sensores MEMS están construidos mayoritariamente con silicio. En la literatura existente, si bien se encuentran varios trabajos que han tratado de corregir el sesgo en sensores inerciales MEMS utilizando técnicas basadas en aprendizaje automático (machine learning), debido a la naturaleza no lineal del fenómeno que se trata de predecir, todos estos trabajos han utilizado sensores MEMS cuyo costo varía entre los USD 300 y los USD 1.000. En los últimos años han surgido sensores inerciales MEMS de muy bajo costo, los cuales se encuentran en una cantidad importante de sistemas robóticos. El costo de estos sensores ronda los USD 10. En este trabajo se propone analizar y compensar las variaciones del sesgo por temperatura en sensores inerciales MEMS de muy bajo costo, usando diferentes técnicas de aprendizaje automático. La hipótesis central de este proyecto se basa en mejorar el rendimiento de sensores inerciales de muy bajo costos aplicando técnicas de aprendizaje automático del estado del arte, las cuales permitirán identificar la relación no lineal que existe entre el sesgo y la temperatura.Red de Universidades con Carreras en Informátic

Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °