Foot-mounted indoor positioning systems work remarkably well when using additionally the knowledge of floor-plans in the localization algorithm. Walls and other structures naturally restrict the motion of pedestrians. No pedestrian can walk through walls or jump from one floor to another when considering a building with different floor-levels. By incorporating known floor-plans in sequential Bayesian estimation processes such as particle filters (PFs), long-term error stability can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians' motion. In this article, a new motion model based on maps and floor-plans is introduced that is capable of weighting the possible headings of the pedestrian as a function of the local environment. The motion model is derived from a diffusion algorithm that makes use of the principle of a source effusing gas and is used in the weighting step of a PF implementation. The diffusion algorithm is capable of including floor-plans as well as maps with areas of different degrees of accessibility. The motion model more effectively represents the probability density function of possible headings that are restricted by maps and floor-plans than a simple binary weighting of particles (i.e., eliminating those that crossed walls and keeping the rest). We will show that the motion model will help for obtaining better performance in critical navigation scenarios where two or more modes may be competing for some of the time (multi-modal scenarios).

Shoe-mounted inertial sensors are widespread deployed in satellite-denied scenarios because of the possibility to re-calibrate stepwise the estimated position. These re-calibrations, known as zero-velocity corrections, prevent an accumulated positioning error growth over time caused by the noise of current medium- and low-cost sensors. However, the error accumulated over time in the height estimation is still an issue under study. The objective of this article is to propose a height correction that is based on the dynamics of the foot. The presented algorithm analyzes the movement of the foot, which is different when walking on horizontal surfaces and stairs. The identification of horizontal surfaces and stairs is detailed in this article. For the assessment of the performance of the proposed height correction, a dataset of approximately 5 h recorded with 10 volunteers walking in a five-story building is employed. The error is evaluated using pre-defined ground truth points. We compare the height error estimated with and without applying the proposed correction and show that the height correction improves the vertical positioning accuracy up to 85.

By incorporating known floor-plans in sequential Bayesian positioning estimators, such as particle filters, long-term positioning accuracy can be achieved as long as the map is sufficiently accurate and the environment sufficiently constrains pedestrians’ motion. Instead of using binary decisions to eliminate particles when crossing a wall, map-based angular probability density functions (PDFs) are used in this article that are capable of weighting the possible headings of the pedestrian according to local infrastructure. In addition, we will include outdoor maps by processing satellite images of the region. We will show that the angular PDFs will help to obtain better performance in critical multi-modal navigation scenarios and in the outdoor area when including maps.

In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

In professional use cases like police or fire brigade missions, coordinated and systematic force management is crucial for achieving operational success during intervention by the emergency personnel. A real-time situation picture enhances the coordination of the team. This situation picture includes not only an overview of the environment but also the positions, i.e., localization, of the emergency forces. The overview of the environment can be obtained either from known situation pictures like floorplans or by scanning the environment with the aid of visual sensors. The self-localization problem can be solved outdoors using the Global Navigation Satellite System (GNSS), but it is not fully solved indoors, where the GNSS signal might not be received or might be degraded. In this paper, we propose a novel combination of an inertial localization technique based on simultaneous localization and mapping (SLAM) with 3D building scans, which are used as prior information, for geo-referencing the positions, obtaining a situation picture, and finally visualizing the results with an appropriate visualization tool. We developed a new method for converting point clouds into a hexagonal prism map specifically designed for our SLAM algorithm. With this combination, we could keep the equipment for first responders as lightweight as required. We showed that the positioning led to an average accuracy of less than 1m indoors, and the final visualization including the building layout obtained by the 3D building reconstruction will be advantageous for coordinating first responder operations.

In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM.