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.

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.

The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

Autonomous positioning in complex urban areas is a challenging problem and has been attracted increasing attention in academia. Multi-sensor fusion of camera, Inertial measurement Unit and wheel odometer has been a prevailing solution that is not only low-cost but easy-to-build. Most of the related methods fuse the odometer measurements into the visual-inertial framework by pre-integrating the wheel speed into the displacement. However, they are not very suitable for some scenarios where the vehicle velocity changes frequently. Instead of pre-integrating the odometer measurements, in this paper, we present an efficient and practical visual-inertial-odometer approach that fuses the wheel speed into the visual-inertial framework directly based on the vehicle motion constraint. Specifically, we use the non-holonomic constraint and lever-arm compensation to introduce the vehicle velocity measurement into the fusion framework to limit the drift. Meanwhile, the proposed framework also allows to online calibrate the IMU-odometer extrinsic parameters explicitly. Moreover, we develop an observability-aware method to enhance the system stability and the performance of online extrinsic calibration. We also develop a robust initialization method to obtain the initial values of visual-inertial-odometer system in one-step optimization. Our approach is validated extensively on simulation environment, autonomous driving public dataset and real world experiment. The simulation results show the extrinsic calibration error is within 0.1 degrees/0.02 m for rotation and translation. The public dataset and real-world ground robot experiments shows a 0.3% position error in both 4 km long urban area route and 403 m long park route.