Simultaneous, Localization and Mapping (SLAM) in Mobile Robots

Dear Colleagues,

Nowadays, mobile robots perform complicated tasks that require navigation in complex and dynamic indoor and outdoor environments, without any human input. In order to autonomously navigate, path plan, and perform these tasks efficiently and safely, simultaneous localisation and mapping (SLAM) is essentially required in order to build an environment map, while simultaneously determining the location of the robot in the map.

In recent years, SLAM has received a fair amount of attention from the research community, and as more and more papers are published in this field, one has to ponder whether the SLAM problem has already been solved? Although excellent progress has been made in the understanding and development of the various frameworks used in the SLAM process, this theoretical progress now drives and inspires the robotics community to deploy SLAM in more and more complex environments, which brings new challenges. Where the advancement of perception sensor technologies, such as the advent of 3D LiDARs, and so on, allows for the acquisition of a high level of semantic details, paving the way for new types of semantic SLAM, the higher processing capability has permitted employing more sophisticated machine learning techniques such as convolutional neural networks (CNN) in the SLAM process, however big data, the hard constraints of real-time operation, and robustness still remain a big issue. Despite having estimators that can handle huge numbers of features and trajectory lengths, failures in data association still have a drastic effect, contorting maps and, ultimately, leading to robots losing their way. There is also a need for new methods to evaluate the quality of the maps and the position estimates, and that whether they have the capability to retrospectively repair poor data association decisions. Similarly, operation in dynamic work spaces is another area that requires great attention. Questions such as “Is lifelong learning really needed?” or “How effectively changes in the environment can be detected and incorporated in the mapping process to increase efficiency and performance?” need to be answered. The objective of this Special Issue is, therefore, to develop a deeper understanding of these major conceptual and technical challenges, and to try to explore suitable solutions for SLAM-based mobile robotics.

Topics of interest include (but are not limited to) the following:

• New algorithms for SLAM (Graph-SLAM, PI-SLAM, β-SLAM, CNN-SLAM, multi-robot or cooperative SLAM, etc.);
• Semantic SLAM;
• Sensing technologies for SLAM applications (LiDAR, Radar, 3D Range Sensors, etc.);
• Development of new SLAM-based applications;
• 3D reconstruction exploiting SLAM applications for safety, navigation, path-planning, traversability, control, and so on.

Prof. Paul Checchin
Dr. Ahmad Kamal Aijazi
Guest Editors

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• Simultaneous localization and mapping (SLAM)
• Map building
• Mobile robotics
• 3D range sensors
• Sensor fusion
• Indoor/outdoor environment mapping