Special Issue "Indoor Navigation in Smart Cities"
Deadline for manuscript submissions: 31 December 2020.
Interests: information systems; smart city, positioning systems and in overall cloud management; urban mobility; service systems
A very evident application area in smart city research is indoor positioning and navigation. Indoor navigation requires specific technologies, since GPS becomes undependable and vague, and public information, like Google maps, is not available. Hence, potential applications should efficiently support a complete service life-cycle, which includes map creation, user positioning, path planning, fixed and moving obstacle avoidance, en-route assistance, etc. Further, potential apps should deal with a variety of indoor spaces, from home to public areas. In turn, potential technologies should encompass fixed and wearable sensors and classic and IOT networks. Finally, apps should serve both normal or disabled persons and interact appropriately.
This Special Issue encourages authors from academia and industry to submit new research results about innovations in indoor navigation. Both research and review papers are welcome. The Special Issue topics include but are not limited to:
- Location-based services and applications;
- Indoor maps and 3D building models;
- Human motion monitoring and modeling;
- Apps and technologies for disabled people;
- Indoor navigation and tracking methods;
- Self-contained sensors;
- Wearable and multisensor systems;
- Intelligent sensors and wireless sensors for smart cities;
- Moving obstacle avoidance;
- Building inspection and maintenance.
Prof. Dr. Gianmario Motta
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation
Authors: Susanna Kaiser
Affiliation: German Aereospace Centre
Abstract: In emergency scenarios, such as a terror 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 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 the successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy and the quality of the map. The results depend on several parameters, e.g. the number of users or sensors, the sensor drifts, the amount of revisited area, and the windows size. We provide a discussion about the choice of the parameters. The results show that even without loop closures the position error can be reduced to ~0.5m when applying partly successive collaborative mapping.