Analysis of inertial measurement accuracy using complementary filter for MPU6050 sensor

Inertial can be defined as disinclination to motion, action, or change. The inertia of an object is the propensity to remain at rest or if in motion, stays in motion at a steady speed. MPU6050 is one of the low-cost motion tracking sensors that contain a 3-axis gyroscope and 3-axis accelerometer orientation measurement. It is used to analyse the movement or location of a person in an indoor environment. This research is to analyse the accuracy of the inertial measurement of the MPU 6050 sensor. Next, is to improve the achievable accuracy rate up to 95% using the complementary filter and finally to visualize the results on an IoT platform. This MPU6050 sensor is beneficial to an emergency responder such as the firefighter’s department. The accurate inertial measurement and location will help to detect the movement and the motion of the firefighter during operation, especially in an indoor environment. The sensor will detect and collects the inertial measurement of an emergency responder and transmit the data wirelessly by using ESP8266 NodeMCU. Finally, the results can be viewed on an IoT platform. However, the results obtained from the MPU 6050 sensor is not perfectly accurate as there is noise during the measurement. Therefore, a complementary filter is used and analysed in this research. It is expected that the inertial location’s accuracy could be improved by 95% that will provide a precise movement and location of the firefighter during operation

Analysis of inertial measurement accuracy using complementary filter for MPU6050 sensor

Inertial can be defined as disinclination to motion, action, or change. The inertia of an object is the propensity to remain at rest or if in motion, stays in motion at a steady speed. MPU6050 is one of the low-cost motion tracking sensors that contain a 3-axis gyroscope and 3-axis accelerometer orientation measurement. It is used to analyse the movement or location of a person in an indoor environment. This research is to analyse the accuracy of the inertial measurement of the MPU 6050 sensor. Next, is to improve the achievable accuracy rate up to 95% using the complementary filter and finally to visualize the results on an IoT platform. This MPU6050 sensor is beneficial to an emergency responder such as the firefighter’s department. The accurate inertial measurement and location will help to detect the movement and the motion of the firefighter during operation, especially in an indoor environment. The sensor will detect and collects the inertial measurement of an emergency responder and transmit the data wirelessly by using ESP8266 NodeMCU. Finally, the results can be viewed on an IoT platform. However, the results obtained from the MPU 6050 sensor is not perfectly accurate as there is noise during the measurement. Therefore, a complementary filter is used and analysed in this research. It is expected that the inertial location’s accuracy could be improved by 95% that will provide a precise movement and location of the firefighter during operation

Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This paper presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index of 3.74%, mismatch distance of the last IMU node being 12.3 mm and relative error of height measurement being -2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance

Similarity-based adaptive complementary filter for IMU fusion

\u3cp\u3eThis paper addresses the attitude estimation problem using vector and gyroscope measurements. We propose a novel adaptation scheme for the complementary filter cut-off frequency which is based on the similarity between independent estimates obtained from the vector and gyroscope measurements. The adaptive complementary filter is also derived on the special orthogonal group and convergence of the filter is established. The effectiveness of our approach is demonstrated with simulation results.\u3c/p\u3

Similarity-based adaptive complementary filter for IMU fusion

This paper addresses the attitude estimation problem using vector and gyroscope measurements. We propose a novel adaptation scheme for the complementary filter cut-off frequency which is based on the similarity between independent estimates obtained from the vector and gyroscope measurements. The adaptive complementary filter is also derived on the special orthogonal group and convergence of the filter is established. The effectiveness of our approach is demonstrated with simulation results