Inertial navigation is a device, which estimates its position, based on sensing external conditions (such as acceleration or angular velocity). It is widely used in variuos applications. Its presence in a drone vehicle for example, allows flight stabilization, by position estimation and feedback-based regulation algorithm execution. A smartphone makes a use of inertial navigation by detecting movement and flipping screen orientation. It is a ubiquitous part of many devices of everyday use, but before using filters and algorithms allowing to calculate the position, a calibration must first be applied to the device. This paper focuses on a separate calibration of each of the sensors - an accelerometer, gyroscope and magnetometer. The further step requires a cross–sensor calibration, and the third step is implementation of data filtration algotithm.

The paper expounds relevant results of some of the present author’s experi- ments defining the strapdown IMU sensors’ errors and their propagation into and within DGPS/IMU. In order to deal with this problem, the author conducted both the laboratory and field-based experiments. In the landborne laboratory the stand-alone Low-Cost IMU MotionPak MKII was verified in terms of the accelerometer bias, scale factor, gyroscope rotation parameters and internal temperature cross-correlations. The waterborne field-trials based on board dedicated research ships at the lake and at the busy small sea harbour were augmented by the landborne ones. These experiments conducted during the small, average, and high dynamics of movement provided comparative sole- GPS, stand-alone DGPS and integrated DGPS/IMU solution error analysis in terms of the accuracy and the smoothness of the solution. This error estimation was also carried on in the context of the purposely-erroneous incipient DGPS/IMU initialisation and alignment and further in the circumstances of on-flight alignment improvement in the absence of the signal outages. Moreover, the lake-waterborne tests conducted during extremely low dynamics of movement informed about the deterioration of the correctly initialised DGPS/IMU solution with reference to the stand-alone DGPS solution and sole- GPS solution. The above-mentioned field experiments have checked positively the DGPS /MKI research integrating software prepared during the Polish/German European Union Research Project and modified during the subsequent Project supported by the Polish Committee for Scientific Research.

Monitoring head movements is important in many aspects of life from medicine and rehabilitation to sports, and VR entertainment. In this study, we used recordings from two sensors, i.e. an accelerometer and a gyroscope, to calculate the angles of movement of the gesturing person’s head. For the yaw motion, we proposed an original algorithm using only these two inertial sensors and the detected motion type obtained from a pre-trained SVM classifier. The combination of the gyroscope data and the detected motion type allowed us to calculate the yaw angle without the need for other sensors, such as a magnetometer or a video camera. To verify the accuracy of our algorithm, we used a robotic arm that simulated head gestures where the angle values were read out from the robot kinematics. The calculated yaw angles differed from the robot’s readings with a mean absolute error of approx. 1 degree and the rate of differences between these values exceeding 5 degrees was significantly below 1 percent except for one outlier at 1.12%. This level of accuracy is sufficient for many applications, such as VR systems, human-system interfaces, or rehabilitation.