Simultaneous Localization and Mapping (SLAM) for Mobile Robot Navigation

Dear Colleagues,

Recent years has seen a surge of mobile robot technologies entering our daily lives. This trend accelerated during the COVID-19 pandemic amplifying the need for automated mobile solutions, e.g. for delivery, surveillance, inspection, or mapping applications. However, for mobile robots to be deployed in a meaningful fashion, they need to be able to navigate safely in dynamic, possibly even unknown, environments and interact naturally with humans. Simultaneous Localization and Mapping (SLAM) is seen as one of the key enablers for the successful deployment of mobile robots.

Despite the popularity of SLAM, it remains a challenging task for SLAM algorithms to work robustly in dynamic, poorly lit, featureless or unknown environments. In fully autonomous operation, data from computer vision, inertial, LiDAR and other time-of-flight sensors are typically coupled with the latest Artificial Intelligence (AI) and Machine Learning techniques such as Gaussian Process Regression and Graph Signal Processing for SLAM algorithms to overcome these technical hurdles.

The aim of this special issue is to present the current state-of-the-art and novel techniques in SLAM enabling future applications of intelligent mobile robots in realistic environments. We look forward to the latest research proposing new algorithms and/or novel applications of SLAM for mobile robot navigation. We invite contributions to the following topics (but not limited to):

• Applications of SLAM for mobile robot navigation
• AI and machine learning for mobile robot navigation
• Map-based or landmark-based navigation
• Vision-based mobile robot navigation
• Data fusion for SLAM-based navigation using vision, inertial, LiDAR, UWB, or other time-of-flight sensors
• Co-operative SLAM
• 3D SLAM for indoor mapping
• Fast SLAM for edge deployment

The use of various sensors such as LiDAR, stereo and mono-vision cameras, and other time-of-flight sensors for SLAM and navigation fits nicely within the scope of the “Sensors” journal and provides opportunities to enable edge devices with increased perception of the environment.

Dr. Henrik Hesse
Dr. Chee Kiat Seow
Dr. Yanliang Zhang
Dr. Torr Polakow
Dr. Kai Wen
Guest Editors

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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.