Abstract—This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic. Index Terms—Information filters, Kalman filtering, machine vision, mobile robot motion planning, mobile robots, recursive estimation, robot vision systems, simultaneous localization and mapping (SLAM), underwater vehicles. I.

Abstract — This article introduces a new approach to Simultaneous Localization and Mapping (SLAM) which pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation which 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 work apart from previous ones: (i) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem; and (ii) 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 previous works. We also describe a practical implementation which aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30.000 m 2, a 2Km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches. Index Terms — Bayesian filtering, hybrid metric-topological maps, loop closure, mobile robots, Rao-Blackwellized particle

particle filters

Abstract — This paper presents a new particle method, with stochastic parameter estimation, to solve the SLAM problem. The underlying algorithm is rooted on a solid probabilistic foundation and is guaranteed to converge asymptotically, unlike many existing popular approaches. Moreover, it is efficient in storage and computation. The new algorithm carries out filtering only in the marginal filtering space, thereby allowing for the recursive computation of low variance estimates of the map. The paper provides mathematical arguments and empirical evidence to substantiate the fact that the new method represents an improvement over the existing particle filtering approaches for SLAM, which work on the joint path state space.

Abstract—Most successful works in Simultaneous Localization and Mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metrictopological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental contributions are: (i) the estimation of the topological path, an improvement similar to that of Rao-Blackwellized Particle Filters (RBPF) and FastSLAM in the field of metric map building; and (ii) the application of grounded methods to the abstraction of topology (including loop closure) from raw sensor readings. It is remarkable that our approach could be still represented as a Bayesian inference problem, becoming an extension of purely metric SLAM. Besides providing the formal definitions and the basics for our approach, we also describe a practical implementation aimed to real-time operation. Promising experimental results mapping large environments with multiple nested loops (~30.000 m 2, ~2Km robot path) validate our work. Index Terms—Mobile robots, Large-scale maps, Loop closure, Rao-Blackwellized Particle Filters, SLAM.

Who has inspired me to be a scientist since t0 vi

Rao–Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active exploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and consequently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims. KEY WORDS—localization, mapping 1.

What is This? Downloaded from ijr.sagepub.com at Universitaetsbibliothek on October 26, 2012Simultaneous localization and mapping with multimodal probability distributions