The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

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.

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, barometer, or magnetometer, which enable the measurement of the step length and the heading direction. In this plan, a method based on IMU/EKF+HMM+ZUPT+ZARU+HDR+the Earth Magnetic Yaw was designed to realize foot-mounted pedestrian navigation. Based on the characteristics of pedestrian navigation, the general likelihood ratio test (GLRT) and the Hidden Markov Model (HMM) were used to realize the detection of zero speed interval at different speed states. When the zero speed state is detected, the zero velocity update (ZUPT) method is used to limit the accumulation of IMU. The Zero Angular Rate Update (ZARU) + (heuristic heading reduction) HDR+the Earth Magnetic Yaw method is used to limit the IMU attitude and heading drift. Finally, the EKF method is used to realize the effective estimation and feedback of the speed, attitude and heading error of the pedestrian navigation system. Meanwhile, a fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements, to improve the filtering effect of EKF algorithm, and ensure the accuracy of pedestrian navigation results.

The Zero-velocity Update (ZUPT) aided Extended Kalman Filter (EKF) is commonly used in the classical INS-based foot-mounted PDR (Pedestrian Dead Reckoning) system. However, in the realistic test, the system still often suffers from drift, which is mainly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and the heading drift which is a poorly observable variable of the ZUPT method. In this paper, in order to overcome these problems, three improved constraint algorithms have been proposed respectively for the detection of the stationary phase, constraint of heading drift and constraint of the height divergence. Firstly, for the problem of failed detection of the stationary phase, a novel stationary phase detection method is proposed which mainly by finding the minimum detector T in each gait cycle to determine the stationary phase, rather than totally based on threshold comparison principle in the traditional method. Comparing with the traditional method, the proposed method can detect the stationary phase of each gait cycle accurately under various pedestrian movements. Secondly, for the heading divergence problem, an improved method is proposed based on the existing HDE (Heuristic Drift Elimination) method, which uses the position error rather than heading error to restrain the trajectory divergence. Comparing with the traditional method, the proposed method can better constrain the heading to the correct angle. At last, for the problem of height divergence, an effective method has been proposed to determine the state of pedestrians by using the slope between adjacent one/several footsteps. At the same time, the slope of the plane and stairs is used to restrict the height divergence. In experiment, the proposed method can be more effectively to distinguish the pedestrian’s state. Especially, when pedestrian walks on the stairs, the slope of the stairs can constrain the height divergence obviously.

The inertial-based pedestrian dead reckoning (PDR) system assisted by zero-velocity update (ZUPT) algorithm has been widely researched, as it can independently provide effective pedestrian's position in indoor environment. However, in the realistic test, the system still often suffers from drift, due to the ZUPT algorithm's poor observation of system's heading. In this article, based on full analysis of the existing maximum range constraint algorithm between both-side feet, first, a constraint method based on the maximum distance of one-side foot has been proposed. It mainly uses the principle that the distance between the position of one-side foot at current time and that of the previous stationary instant should be within a maximum range. Second, a one-side foot and both-side feet cooperative maximum range constraint algorithm has been proposed. It mainly uses the one-side foot maximum range and the both-side feet maximum range to intersect the most likely position of pedestrian's current moving foot and then uses this position to constrain the heading divergence. Finally, to further improve the heading constraint accuracy, a maximum range decoupling algorithm has been proposed, which decouples the maximum ranges of one-side foot and both-side feet into the distance in the forward direction. Then, using this distance, the heading divergence can be constrained more effectively.