Mobile robots can be used in numerous outdoor tasks such as patrolling, delivery and military applications. In order to deploy mobile robots in this kind of environment, where there are different challenges like slopes, elevations, or even holes, they should be able to detect such challenges and determine the best path to accomplish their tasks. In this paper, we are proposing an exploration approach based on potential fields with local distortions, in which we define preferences in uneven terrains to avoid high declivity regions without compromising the best path. The approach was implemented and tested in simulated environments, considering a ground robot embedded with two 2D LIDAR sensors, and the experiments demonstrated the efficiency of our method.

Many applications with mobile robots require self-localization in indoor maps. While such maps can be previously generated by SLAM strategies, there are various localization approaches that use 2D floor plans as reference input. In this paper, we present a localization strategy using floor plan as map, which is based on spatial density information computed from dense depth data of RGB-D cameras. We propose an interval-based model, called Interval Free-Space Density, that bounds the uncertainty of observations and minimizes the effects of movable objects in the environment. Our model was applied in a Monte Carlo Localization strategy and compared with traditional observation models. The results of experiments showed the robustness of the proposed method in single-camera and multi-camera experiments in home environments.