2.1. Multisensor IntegrationTo represent a robot’s surrounding terrain in a virtual environment, it is necessary to reconstruct a terrain model using an integrated dataset obtained from multiple sensors then [8�C12]. Rovira-M��s [13] proposed a density grid for 3D reconstruction from information obtained from stereo cameras, a localization sensor, and an inertial measurement unit. Sukumar [3] provided a convenient visualization method by Inhibitors,Modulators,Libraries integrating sensed datasets into a textured terrain mesh. However, it is difficult for these systems Inhibitors,Modulators,Libraries to process the large datasets obtained in outdoor environments and achieve on-line rendering.Other researchers have enhanced the performance of terrain reconstruction to provide on-line photo-realistic visualization. Kelly [9] describes real-world representation methods using video-ranging modules.
In the near Inhibitors,Modulators,Libraries field, 3D textured voxel grids are used to describe the surrounding terrain, whereas a billboard texture in front of the robot is used to show scenes in the far field. However, a range sensor cannot sense all terrain information, often leaving empty spaces in the terrain model in Inhibitors,Modulators,Libraries practice.2.2. Interpolation in Empty RegionsRecovery of these ��unsensed�� regions plays a major role in obstacle avoidance. Some researchers apply interpolation algorithms to fill empty holes and smooth terrain [14�C17]. For example, to estimate such unobserved data, Douillard [18] interpolates grids in empty regions in elevation maps in order to propagate label estimates. However, it is difficult to use these methods to recover missing information that is beyond the measurement range of the sensors.
Wellington [19] applies a hidden semi-Markov model to classify terrain vertical structure into ground, trees, Dacomitinib and free space classes for each cell of a voxel-based terrain model. Then an MRF algorithm is used to estimate ground and tree height. However, this height estimation process simply averages across cells using neighbor data and cannot estimate actual height values.In hardware selleck products design research, Fr��h [7] utilizes a vertical 2D laser scanner to measure large buildings and represent streetscapes in urban environments. When an object is located between the sensors and a building, some regions of the building cannot be sensed by the laser scanner as they are blocked by the object. These missing regions can be easily filled by planar or horizontal interpolation algorithm.2.3. Traversable Region SegmentationGround segmentation is a widely studied topic necessary to determine the traversable regions in a terrain. Pandian [2] classifies terrain features into rocky, sandy, and smooth classes solely from 2D images. The segmented results take the form of a rectangular grid, instead of polygon shape. Therefore, this method lacks precision.