# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 1 Introduction  
Autonomous navigation of unmanned ground vehicles (UGVs) requires detailed and updated information on the environment obtained from onboard sensors [1]. Laser rangefinders are sensors commonly employed to obtain three-dimensional (3D) point clouds of the area where the mobile robot moves. Thus, these kinds of sensors are used by UGVs for off-road navigation [2], for planetary exploration [3], urban search and rescue [4] or for agricultural applications [5].  
The processing of outdoor scenes acquired with a 3D laser scanner is very different from object scanning [6], since in any direction and at any distance relevant data can be found. In addition, there may be more occlusions, erroneous ranges or numerous points from unconnected areas. In urban areas and indoors, processing of laser information can benefit from the recognition of common geometric features, such as planes [7]. Natural terrain classification can be performed by computing saliency features that capture the local spatial distribution of 3D points [8, 9].  
In past years, 3D data acquisition from UGVs was usually performed in a stop-and-go manner [10, 11], but, nowadays, it can be performed in motion by using some kind of simultaneous localization and mapping (SLAM) by combining data from odometry [12] or from an inertial unit [13].  
A leveled 3D point cloud is a representation of the surroundings that can be used for local navigation [14]. Elevation maps offer a compact way of representing 3D point clouds with a 2.5-dimensional data structure [15]. Moreover, fuzzy elevation maps (FEMs) can operate with measurement uncertainty of the sensors [16, 17]. However, fuzzy interpolation can produce unreliable solutions in areas with sparse measurements, and it is necessary to use a fuzzy reliability mask (FRM) for each FEM [18, 19].  
Navigation on natural terrains can be performed by following distant goal points given by their geographic coordinates [20, 21]. These waypoints can be planned based on 2D terrestrial maps or aerial images [22], and represents the trajectory that, broadly, the vehicle should follow. However,to navigate between distant goal points, the robot must avoid local obstacles [23]. For this purpose, reactive navigation based on the evaluation of elementary movements in terms of the risks of traversing the terrain have been proposed [24; 25; 26].  
This paper describes a case study of outdoor navigation using FEMs with the mobile robot Andabata (see Figure 1). All in all, the paper offers three main contributions:  
* A complete field navigation system developed on the onboard computer under the Robot Operating System (ROS) [27] is presented.
* 3D scan acquisition with a continuous rotating 2D laser scanner [28] is performed with a local SLAM scheme during vehicle motion.
* The computation of FEMs and FRMs is sped up using least squares fuzzy modeling [29] and multithreaded execution of nodes among the cores of the processor.  
The paper is organized as follows. The next section presents the mobile robot Andabata including its 3D laser sensor. Section 3 explains how leveled 3D point clouds are obtained while the robot is moving. Then, Section 4 details the filter applied to the leveled 3D point cloud. The construction of the FEM and FRM with the filtered 3D point cloud is described in Section 5. The multithreaded computing of FEMs and FRMs under ROS is discussed in Section 6. Section 7 presents the field navigation strategy of Andabata and some experimental results. The paper ends with conclusions, acknowledgements and references.