Monte Carlo localization (MCL) uses a reference map to estimate a pose of a ground robot in outdoor environments. However, MCL shows low performance when it uses an elevation map built by an aerial mapping system because three-dimensional (3D) environments are observed differently from the air and the ground and such an elevation map cannot represent outdoor environments in detail. Although other types of maps have been proposed to improve localization performance, an elevation map is still used as the main reference map in some applications. Therefore, we propose a new feature to improve localization performance with an elevation map. This feature is extracted from 3D range data and represents the part of an object that can be commonly observed from both the air and the ground. Therefore, this feature is likely to be accurately matched with an elevation map, and the average error of this feature is much smaller than that of unclassified sensing data. Experimental results in real environments show that the success rate of global localization increased and the error of local tracking decreased. Thus, the proposed feature can be very useful for localization of an outdoor ground robot when an elevation map is used as a reference map.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications