# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 1 Introduction
### Related Works on Unsupervised Learning Approaches to Traversability Prediction  
As an input for the traversability prediction, LiDAR (either 2D [13] or 3D [9; 14]) and stereo camera images [15; 16; 17] have been widely used. Shape attributes of the ground have been extensively investigated for traversability estimation. The idea is to use 3D environment analysis methods and chart the mobility smoothness in a hierarchical manner. Based on this, 3D Ladar information has been clustered according to scatterness (grass), surfaceness (ground), and linearness (tree trunks) for terrain modeling [18].  
Although laser sensors are more precise than stereo cameras, they are still costly and do not necessarily provide sufficient precision due to the sparseness of the 3D points. In particular, they are not perfectly able to detect small unevenness in the terrain in front of a robot despite their high applicability for detecting obstacles with sufficient size and height. Compared with laser sensors, cameras are more affordable and provide different attributes of terrain from 3D geometry obtained through point clouds. Thus, it is important to pursue the availability of image information for the prediction of traversability. In this paper, we investigate the applicability of 2D image features obtained by a camera for traversability prediction.  
From the viewpoint of output as a measure of traversability for the self-supervised learning, the discrimination of traversable/non-traversable terrains [10; 16] and the classification of terrains, such as gravel/sand/grass [19; 20; 21; 22], are frequently used. One problem of discriminating traversable/non-traversable terrains as the output of the classifier is that the boundary between the two classes is inconsistent and depends on the environment or requirements on the robot. When all regions around a robot are classified as traversable, the robot might still prefer to select a path with minimum (small) damage to its body considering the accumulation of fatigue during a long operational period. In contrast, even if every region around it is classified as non-traversable, the robot might have to search for the least hazardous path to escape from a situation. Thus, the predictability of a continuous cost is more important for avoiding damage to the robot. From the same viewpoint, classifying terrains (gravel/sand/etc.) is not the ultimate goal of traversability prediction; however, cost/damage to the robot should be considered.  
As continuous measures of traversability assessment, the slippage of wheels of planetary rovers [6] and the deformability of rocky terrains [11; 23] have been investigated. Considering the general use of mobile robots in outdoor environments, however, it is useful to consider not only large-scale unevenness, such as rocky and slippy terrains, but also small-scale unevenness, which causes vibrations or (vertical) acceleration. By considering such small-scale unevenness, it becomes possible to reduce accumulated damage to the robot and instability of its cargo and improve the comfort of passengers.