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.

This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m 2 , a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.Index Terms-Bayesian filtering, hybrid metric-topological (HMT) maps, loop closure, mobile robots, Rao-Blackwellized particle filters (RBPFs), simultaneous localization and mapping (SLAM), topological maps. NOMENCLATURE m HMT map (an annotated graph). a M Local metric map for the area a. b a ∆ Coordinate origin of area b relative to that of area a. s t Robot HMT pose at time step t. u t , o t Robot actions and hybrid observations at time step t. s t , u t , o t Sequences of robot poses, actions, and observations for time steps 1 to t. i s t , i u t , i o t A convenient way of referencing the robot poses, actions, and observations grouped into the area i such that the first elements are given for t = 0. ). This material includes two videos (HMT-SLAM malaga.avi; HMT-SLAM edmonton.avi) demonstrating the application of University of Málaga's Hybrid Metric-Topological (HMT) SLAM method to the Málaga Campus dataset and Edmonton dataset, respectively. The first video shows how the robot closes a number of large loops in a large scale (2 km path), nested loop environment. The second video shows how the robot maps a midsized environment with one loop. The first video is of size 28.8 MB while the second is of 19.3 MB. Contact jlblanco@ctima.uma.es for further questions about this work.Color versions of one or more of the figures in this paper are available online at Sequences of all the corresponding variables up to time step t. ψ t , z t Area-dependant and metric observations, respectively. γ t , x t Topological and metric parts of s t at time step t, respectively. γ t Topological path of the robot up to time step t. Υ t Set of all known areas at time step t. s[k ] t kth particle at time step t for the robot HMT pose. ω[k ] t Importance weight of the kth particle at time step t.

The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download

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This paper addresses the problem of estimating the spatial distribution of volatile substances using a mobile robot equipped with an electronic nose. Our work contributes an effective solution to two important problems that have been disregarded so far: First, obstacles in the environment (walls, furniture,…) do affect the gas spatial distribution. Second, when combining odor measurements taken at different instants of time, their 'ages' must be taken into account to model the ephemeral nature of gas distributions. In order to incorporate these two characteristics into the mapping process we propose modeling the spatial distribution of gases as a Gaussian Markov random field. This mathematical framework allows us to consider both: (i) the vanishing information of gas readings by means of a time-increasing uncertainty in sensor measurements, and (ii) the influence of objects in the environment by means of correlations among the different areas. Experimental validation is provided with both, simulated and real-world datasets, demonstrating the out-performance of our method when compared to previous standard techniques in gas mapping.