We have been focused on the exploitation of the limits of the performance of low cost sensors, used in an integrated way, both for classical and geodetic navigation. In this work, the navigation performance of the Android smartphone’s IMU was assessed thoroughly. The raw IMU data from two smartphones, “Xiaomi 8” and “Honor Play” were recorded, using different sampling rates, and compared with the measurements, recorded simultaneously from higher grade IMU’s. For assessing the navigation capabilities of the smartphones IMU sensors, two kinematic tests were designed. In these tests, the smartphones IMU data were processed using a 15-state Kalman Filter developed by our group, while the reference IMU data were processed using a commercial software. The details of the comparisons between the positioning, velocity and attitude solutions are discussed. The results show that the navigation solutions of both smartphones are quite close to that of the reference IMUs’, showing differences of around 0.2 m in positioning, 0.3 m/s in velocity, and 1° in roll and pitch. The quality of the yaw estimation, with an accuracy of a few degrees, is clearly worse than that of the other attitude parameters. However, the results of the second test reveal that using a higher data rate setting for the IMU samplings, it is possible to improve the solution. The assessment study of the smartphone’s IMUs performed in this work can be useful for the development of many different applications, including the exploitation for Autonomous Vehicles, Intelligent Transport Systems, and Smart Cities.

Recent study indicates that by using the inertial measurement unit (IMU) sensors inside smartphones, we can obtain similar navigation solutions to the professional ones. However, the sampling rates of the gyros and accelerometers inside some types of smartphones are not set in the same frequencies, i.e., the gyros of “Huawei p40” are in 50 Hz while the accelerometer is 100 Hz. The conventional method is resampling the higher frequency to the lower frequency ones, which means the resampled accelerometer will lose half frequency observations. In this work, a modified Kalman filter was proposed to integrate all these different rate IMU data in the GNSS/IMU-smartphone coupled navigation. To validate the proposed method, a terrestrial test with two different types of android smartphones was done. With the proposed method, a slight improvement of the attitude solutions can be seen in the experiments under the GNSS open-sky condition, and the obvious improvement of the attitude solutions can be witnessed at the simulated GNSS denied situation. The improvements by 45% and 23% of the horizontal position accuracy can be obtained from the experiments under the GNSS outage of 50 s in a straight line and 30 s in a turning line, respectively.

A simple and inexpensive POS for application to light airborne platforms was developed, and a modified Kalman Filtering was designed to integrate GNSS/IMU/Image data, which uses the relative orientation information to improve the accuracy of the exterior orientation parameters. The precise relative orientation of conjugate images was obtained using a SIFT/SFM matching algorithm. The relative exterior orientations were transformed from the camera frame to the navigation frame before they were used as external update information of the Kalman Filter. Combining all the relative orientation information retrieved from images, the Kalman Filter can give an improved output for the exterior orientation parameters. Airborne result from the tests of one straight strip shows that the heading accuracy of a GNSS/MEMS-IMU was improved from 0.26° to 0.10°, and the result from one closed strip shows that heading accuracy was improved from 1.11° to 0.85° and roll accuracy from 0.54° to 0.43°.