**1. Introduction**

A great deal of the investigation done in the field of robotics is addressed to the Simultane‐ ous Localisation and Mapping (SLAM) problem [1, 2]. The SLAM problem is generally described as that of a robot—or robotic device with exteroceptive sensor/s—which explores an unknown environment, performing two different tasks at the same time: It builds a map with the observations obtained through the exteroceptive sensor/s [3] and localizes itself into the map during the exploration, thus knowing the position and trajectory.

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The works defining the origin of the field can be traced to Smith and Cheeseman [4], Smith et al. [5], and Durrant-Whyte [6], which established how to describe the relationships between landmarks while accounting for the geometric uncertainty through statistical methods. These eventually led to the breakthrough represented in Smith's work. In such a research, the problem was presented for the first time as a combined problem with a joint state composed of the robot pose and the landmark estimations. These landmarks were considered correlated due to the common estimation error on the robot pose. That work would lead to several works and studies, being [7] the first work to popularize the structure and acronym of SLAM as known today.

The problem related with SLAM techniques is considered of capital importance given that a solution to it is required to allow an autonomous robot to be deployed in an unknown environment and operate without human assistance. But there is a growing field of robotics research that deals with the interaction of human and robotic devices [8]. Thus, there are several applications of robotic mapping and navigation that include the human as an actor. The basic application would be the exploration of an environment by a human, but mapped through a robotic platform [9]. Other works deal with more complex applications, such as mapping the trajectory of a group of humans and robots during the exploration of an environment and coordinating them with the help of radio frequency identification (RFID) tags [10]. Another application gaining weight is the use of SLAM to allow assistance robots to learn environments, improving the usability of the device [11].

All these approaches solve some kind of SLAM problem variant where the human factor is present: to assist, to track, to navigate, etc. But none uses data captured by human senses. There are works that deal with the mapping of human-produced data into map generated by a robot, but these data are not used in the map estimation process, but 'tagged' to it. So currently, no approach uses the data from human into the solution to the SLAM problem. This is a waste of useful resources, given the power of the human sight, still superior in terms of image proc‐ essing to the most advanced techniques which are increasingly adopting the strategies discovered by scientists, but designed and adopted by human evolution millennia ago.

So, in this chapter, we will discuss about the monocular SLAM problem in the context of human-robot interaction (HRI), with comments on available sensors and technologies, and different SLAM techniques. To conclude the chapter, a SLAM methodology where a human is part of a virtual sensor is described. His/her exploration of the environment will provide data to be fused with that of a conventional monocular sensor. These fused data will be used to solve several challenges in a given delayed monocular SLAM framework [12, 13], employing the human as part of a sensor in a robot–human collaborative entity, as was first described in authors' previous work [14].
