We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of these field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient computationally. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT*-based search for the nearest frontier and also against a locally optimal exploration strategy.

Abstract-Current solutions to the simultaneous localization and mapping (SLAM) problem approach it as the optimization of a graph of geometric constraints. Scalability is achieved by reducing the size of the graph, usually in two phases. First, some selected nodes in the graph are marginalized and then, the dense and non-relinearizable result is sparsified. The sparsified network has a new set of relinearizable factors and is an approximation to the original dense one. Sparsification is typically approached as a Kullback-Liebler divergence (KLD) minimization between the dense marginalization result and the new set of factors. For a simple topology of the new factors, such as a tree, there is a closed form optimal solution. However, more populated topologies can achieve a much better approximation because more information can be encoded, although in that case iterative optimization is needed to solve the KLD minimization. Iterative optimization methods proposed by the state-of-art sparsification require parameter tuning which strongly affect their convergence. In this paper, we propose factor descent and non-cyclic factor descent, two simple algorithms for SLAM sparsification that match the state-of-art methods without any parameters to be tuned. The proposed methods are compared against the state of the art with regards to accuracy and CPU time, in both synthetic and real world datasets.

Abstract-Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dualtimescale approach. The proposed approach -Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT-NDT-MCL) -is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously proposed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.

We propose a novel method for robotic exploration that evaluates paths that minimize both the joint path and map entropy per meter traveled. The method uses Pose SLAM to update the path estimate, and grows an RRT* tree to generate the set of candidate paths. This action selection mechanism contrasts with previous appoaches in which the action set was built heuristically from a sparse set of candidate actions. The technique favorably compares agains the classical frontier- based exploration and other Active Pose SLAM methods in simulations in a common publicly available dataset.Peer ReviewedPostprint (author's final draft

In the context of graph-based simultaneous localization and mapping, node pruning consists in removing a subset of nodes from the graph, while keeping the graph's information content as close as possible to the original. One often tackles this problem locally by isolating the Markov blanket sub-graph of a node, marginalizing this node and sparsifying the dense result. It means computing an approximation with a new set of factors. For a given approximation topology, the factors' mean and covariance that best approximate the original distribution can be obtained through minimization of the Kullback-Liebler divergence. For simple topologies such as Chow-Liu trees, there is a closed form for the optimal solution. However, a tree is oftentimes too sparse to explain some graphs. More complex topologies require nonlinear iterative optimization. In the present paper we propose Factor Descent, a new iterative optimization method to sparsify the dense result of node marginalization, which works by iterating factor by factor. We also provide a thorough comparison of our approach with state-of-the-art methods in real world datasets with regards to the obtained solution and convergence rates.