## 2 State of the Art  
As already mentioned, the agricultural sector is one of the least technologically developed. Nonetheless, some robotic systems have been already proposed for harvesting [4; 5; 6; 7] and for weed  
Figure 1: Sample of small farms, including abandoned ones, in Valencia (Spain). control/elimination [8; 9; 10; 11]. All these tasks have a common element: the robotic systems should autonomously navigate. A major part of related works on autonomous mapping is based on remote imagery. An interesting review can be found in Reference [2]. However, as the authors stated, hyperspectral images produce highly accurate maps but this technology might not be portable because of the prohibitive operating costs. Similarly, other approaches (e.g., imagery provided by unmanned aerial vehicles (UAVs) [12] or airborne high spatial resolution hyperspectral images combined with Light Radar (LiDAR) data [13]) also have high operating costs. [PERSON] et al. [3] introduced a classification system to perform mapping tasks on different types of groves using airborne colour images and orthophotos. A semi-autonomous navigation system controlled by a human expert operator was introduced by [PERSON] et al. [14]. This navigation was proposed to supervise the path planning and navigation of a fleet of tractors using the information provided by vision sensors. During its execution, the system determines the route segment where each tractor should drive trough to perform crop spraying. Each segment is assigned by tracking positions and speeds previously stored. When an obstacle is detected, the tractor stops and the human operator decides the actions to be carried out. A sensor fusion system using a fuzzy-logic-enhanced Kalman Filter was introduced by [PERSON] et al. [15]. The paper aimed to fuse the information of different sensor systems for pathfinding. They used machine vision, LiDAR, an Inertial Measurement Unit (IMU), and a speedometer to guide an autonomous tractor trough citrus grove alleyways. The system was successfully tested on curved paths at different speeds. In the research introduced by [PERSON] et al. [16], a set of binary detectors were implemented to detect obstacles, tree trunks, and tree lines, among other entities for off-road autonomous navigation by an unmanned ground vehicle (UGV). A compact terrain map was built from each frame of stereo images. According to the authors, the detectors label terrain as either traversable or non-traversable. Then, a cost analysis assigns a cost to driving over a discrete patch of terrain. [PERSON] et al. [17] introduced a _Real Time Kinematic Differential Global Positioning System_-based autonomous field navigation system including automated headland turns. It was developed for crop-row mapping using machine vision. Concretely, they used a colour camera mounted at 0.9 m above the ground with an inclination of 37\({}^{\circ}\) for the vertical. Results from the field experiments showed that the system can be guided along a defined path with cm precision using an autonomous robot navigating in a crop row. A navigation system for outdoor mobile robots using GPS for global positioning and GPRS for communication was introduced in Reference [18]. A server-client architecture was proposed and its hardware, communication software, and movement algorithms were described. The results on two typical scenarios showed the robot manoeuvring and depicted the behaviour of the novel implemented algorithms. [PERSON] et al. [19] proposed a framework to build globally referenced navigation grids by combining global positioning and stereo vision. First, the creation of local grids occurs in real time right after the stereo images have been captured by the vehicle in the field. In this way, perception data is effectively condensed in regular grids. Then, global referencing allows the discontinuous appendage of data to succeed in the completion and updating of navigation grids over time over multiple mapping sessions. With this, global references are extracted for every cell of the grid.