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Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.

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.

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