Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

In recent years, as smartphones have widely spread in the globe, it can be said that billions of users are carrying with high performance computer being always powered on. Since most of the advanced smartphones are equipped with motion sensors (accelerometers, gyroscopes and magnetometers) which are prerequisites for pedestrian dead reckoning, or PDR, it can be highly expected to realize seamless indoor/outdoor localization of the pedestrian users. However, the smartphones can be held in many ways such as in users' hands, pockets or handbags and they can be also subject to several complicated motions other than walking locomotion. In this research, we have aimed at providing technologies to estimate location and orientation (attitude) of the smartphones in users' hands, pockets and handbag even in the presence of several complex motions such as one caused by swinging his/her arms or the handbag. Since human walking locomotion has discriminative and repeating patterns in the components of acceleration and angular rate in the moving (walking) direction and the vertical/lateral direction even in the presence of other additional motions such as one caused by swinging arms, these patterns can be detected and extracted in the frequency domain analysis.

We present a method of estimating the location and orientation of a pedestrian which simultaneously recognizing his/her actions with a single low-cost inertial measurement unit (IMU) mounted at the waist of the user. Some of the actions other than walking locomotion, such as standing up from/sitting down on a chair, and bending over to slip through obstacles, taken by the pedestrians can be mostly seen at the particular locations where the objects and building facilities to induce the actions are placed. Conversely, by knowing the current location and its attribute about possibly taken actions, the action recognition process can be improved with the contextual information since prior knowledge about occurrence of actions is given as the attribute in the map. Additionally, when the posture (such as sitting, standing and getting to one knee) of the pedestrians is known, falsely recognized actions can be rejected. Experimental results show that accuracy of the action recognition on six types of the action (forward walking, backward walking, side stepping, sitting down on/standing up from a chair, going downstairs/upstairs and bending over) is more than 95% by cross validation test on the training data set, and the results also show that error rate of the PDR localization is reduced from 4% of the walking distance to 2% in the total scenario within the office environment by using the results of action recognition to adjust the estimated location.