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

Abstract

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 °.

Performance Evaluation of IMU and DVL Integration in Marine Navigation

“…As the CSM-MG100 is already packaged, and the details of the sensors used are unknown, we estimated the accuracy of the sensor using Allan variance (AV) analysis. Moreover, a more detailed profile from a specific unit is required to later use the level of the sensor noise to configure a KF, which will be part of an integrated navigation system (INS/DVL) [ 12 ]. Static bias, standard deviation (STD), angle velocity random walk, and bias instability are shown in Table 1 .…”

“…Many researchers have studied single-IMU dead-reckoning navigation and optimized algorithms to reduce error accumulation in IMUs [ 4 , 9 , 10 , 11 , 12 , 13 , 14 ]. Low-pass filtering has been used to optimize the calculation results of low-cost shipboard IMUs [ 4 ].…”

“…However, the typical error in these systems remains over 100 m after 10 min. The influence of ship vibration, wind, and waves in the offshore environment causes significant noise in IMU attitude updating [ 11 , 12 ]. A “virtual vertical reference” method combined with a compass has been proposed to reduce IMU error divergence [ 13 ].…”

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

“…The roll angle of agricultural machine can be measured in multiple ways [5][6][7][8][9], and the commonly used method is based on inertial sensors. Inertial sensors generally include accelerometers and gyroscopes [10][11][12][13] or inclination sensors [14,15]. For instance, an external acceleration inclination angle Kalman filter algorithm was proposed for agricultural machinery [1].…”

“…For instance, an external acceleration inclination angle Kalman filter algorithm was proposed for agricultural machinery [1]. Two SensComp 600 ultrasonic sensors were used to measure the reference level heights at two sides of flat shovels respectively, and the inclining angles of flat shovels were determined according to geometrical relations [13].…”