Simultaneous Location and Mapping (SLAM) Focused on Mobile Robotics

Dear Colleagues,

I am pleased to introduce this Special Issue on “Simultaneous Location and Mapping (SLAM) Focused on Mobile Robotics”. This Special Issue is of fundamental importance in the current robotics area for at least three main reasons. First of all, environment mapping is paramount for the Perception Action (PA) loop in many robotics applications and Augmented Reality (AR) human–robot–environment interaction. In the last few years, new and emerging technologies have enabled the acquisition of high-quality and robust maps in several scenarios with low effort in terms of both budget and coding. Last, but not least, new algorithms have shown that the drift problem can be drastically reduced using new, non-recursive, approaches or cooperative methods. As a consequence, we face a great opportunity to spread robotics applications to several new fields where, due to technology limitations or budget restrictions, robotics have not yet been properly exploited.

The Perception–Action loop is basic in control and robotics applications where the variable to control, or the object to reach and manipulate/pick, must, first of all, be sensed, i.e., perceived. For modern and ever-more complex robotics and artificial intelligence-based mobile robotics applications, context awareness is fundamental along with the possibility of locating an agent within a current and updated map. When the man is one of the agents within the loop, thanks to Augmented Reality technologies, the map of landmarks and/or the 3D reconstruction of the environment, together with ego-location, are able to enable high fidelity and fully-immersive augmented human perception experiences.

Emerging technologies embed several sources of information, such as traditional cameras, stereo, 3D time-of-flight cameras, gyroscopes, and magnetometers with processing capabilities. A real-time algorithm running onboard a proper DPU, GPU or neural network is able to then fuse that information in order to provide a map and location inside a currently updated map. Examples comprise Microsoft HoloLens, Intel RealSense, and other solutions that are able to recover an ambient map after just a quick tour inside it.

Furthermore, the algorithms serving as the basis of SLAM were, for many years, based on recursive Kalman-like filtering that, unfortunately, was not able to solve the drift problem. Mobile robotics are, in fact, prone to drift because mobile robots are non-holonomous systems. One current solution exploits graph theory to simultaneously take into consideration the signal correlation among all the robot routes. Other solutions make use of several agents equipped with different acquisition systems in a collaborative approach.

The objective of this Special Issue is, therefore, to promote a deeper understanding of major conceptual and technical challenges, and to facilitate the spread of recent breakthroughs in SLAM for mobile robotics. This Special Issue, by achieving this objective, is expected to spread the state-of-the-art to new frontiers of robotics applications.

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

• New algorithms for SLAM (G-SLAM, multivehicle/cooperative SLAM, etc.)
• Sensing technologies for SLAM
• Map and location output metrological calibration
• Development of new applications enabled by emerging technologies (Microsoft HoloLens, Intel RealSense, etc.)
• Augmented Reality to exploit SLAM applications for vehicle safety in path planning and control

• Simultaneous Location And Mapping
• Map building for Augmented Reality
• Mobile Robotics

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