Applying computer vision feature detectors and descriptors to occupancy grids has important practical applications for the problem of grid map matching in mobile robot localization and mapping, although this approach has received little attention by the community. This review presents a thorough performance evaluation for several combinations of detectors (Harris, KLT, SIFT and SURF) and descriptors (SIFT, SURF and circular patches) using maps obtained from real datasets. It is shown how a combination of the Harris or KLT detector with circular patch descriptors provides the best results in both computation time and classification success ratio.

This work aims at improving real-time motion control and dead-reckoning of wheeled skid-steer vehicles by considering the effects of slippage, but without introducing the complexity of dynamics computations in the loop. This traction scheme is found both in many off-the-shelf mobile robots due to its mechanical simplicity and in outdoor applications due to its maneuverability. In previous works, we reported a method to experimentally obtain an optimized kinematic model for skid-steer tracked vehicles based on the boundedness of the instantaneous centers of rotation (ICRs) of treads on the motion plane. This paper provides further insight on this method, which is now proposed for wheeled skid-steer vehicles. It has been successfully applied to a popular research robotic platform, pioneer P3-AT, with different kinds of tires and terrain types.

In this paper we consider the problem of creating a spatial representation of a gas distribution in an environment using a mobile robot equipped with gas sensors. The gas distribution mapping method used models the information content of a given measurement about the average concentration distribution with respect to the point of measurement. In this paper, we present an extension which can consider the uncertainty about the robot's position in the gas distribution mapping. We present a preliminary result where a mobile robot equipped with gas sensors creates a map of a large indoor environment, using both spatial and olfactory information. I. INTRODUCTION Creating a spatial representation of a gas distribution in an environment is an important and challenging subprob- lem within the field of mobile olfaction. Gas distribution mapping (GDM) could be used as a means to determine the exact location of gas sources or perhaps even more importantly, determine areas of high concentrations of a harmful gas (that may not always be present at the source location). Hindered by the temporally fluctuating character of turbulent gas transport, and the fact that chemical gas sensors provide information only about the small volume their surface interacts with, it is probably impossible to measure the instantaneous concentration field without using a dense grid of sensors. Nonetheless, it is often sufficient to know the time-constant structure of a gas distribution for many applications such as air quality monitoring and surveillance of industrial sites. Furthermore, by using mobile robots to map the gas distribution, contaminated area could be examined in rescue missions in order to provide incident planning staff with information to prevent rescue workers from being harmed or killed due to explosions, asphyxiation or toxication. The contribution of this paper is a description of a gas distribution mapping algorithm which is able to take into account the uncertainty in the pose estimate. This is an important aspect to consider that is inherent to a real robot moving in an unknown environment. In probabilistic estima- tion theory applied to the SLAM problem, Bayesian filtering