Topological and metrical methods for representing spatial knowledge have complementary strengths. We present a hybrid extension to the Spatial Semantic Hierarchy that combines their strengths and avoids their weaknesses. Metrical SLAM methods are used to build local maps of small-scale space within the sensory horizon of the agent, while topological methods are used to represent the structure of large-scale space. We describe how a local perceptual map is analyzed to identify a local topology description and is abstracted to a topological place. The mapbuilding method creates a set of topological map hypotheses that are consistent with travel experience. The set of maps is guaranteed under reasonable assumptions to include the correct map. We demonstrate the method on a real environment with multiple nested large-scale loops.

We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real-time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.

This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on real-world data. 1

Abstract—While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding general-purpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies, given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, sample-based representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies, and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markov-chain Monte Carlo algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements. Index Terms—Bayesian inference, Markov-chain Monte Carlo (MCMC), mobile robots, perceptual aliasing, probability distributions, sample-based representations, topological maps. I.

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random fields. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy.

This article presents an approach to SLAM that takes advantage of panoramic images. Landmarks are interest points detected and matched in the images and mapped according to a bearings-only SLAM approach. As they are acquired and processed, the panoramic images are also indexed and stored into a database. A database query procedure, independent of the robot and landmark position estimates, is able to detect loop closures by retrieving memorized images that are close to the current robot position. The bearings-only estimation process is described, and results over a trajectory of a few hundreds of meters are presented and discussed. 1

Abstract — Unknown, unexplored and abandoned subterranean voids threaten mining operations, surface developments and the environment. Hazards within these spaces preclude human access to create and verify extensive maps or to characterize and analyze the environment. To that end, we have developed a mobile robot capable of autonomously exploring and mapping abandoned mines. To operate without communications in a harsh environment with little chance of rescue, this robot must have a robust electro-mechanical platform, a reliable software system, and a dependable means of failure recovery. Presented are the mechanisms, algorithms, and analysis tools that enable autonomous mine exploration and mapping along with extensive experimental results from eight successful deployments into the abandoned Mathies coal mine near Pittsburgh, PA. I.

Abandoned mines pose significant threats to society, yet a large fraction of them lack accurate maps. This article discusses the software architecture of an autonomous robotic system designed to explore and map abandoned mines. We have built a robot capable of autonomously exploring abandoned mines. A new set of software tools is presented, enabling robots to acquire maps of unprecedented size and accuracy. On May 30, 2003, our robot “Groundhog” successfully explored and mapped a main corridor of the abandoned Mathies mine near Courtney, PA. The article also discusses some of the challenges that arise in the subterraneans environments, and some the difficulties of building truly autonomous robots.

particle filters

Abstract — This paper presents a metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The contribution is a geometric distance that takes into account the translation and orientation of the sensor at the same time. This result is used in the two steps of the matching- estimation process. The correspondences between scans are established with this measure and the minimization of the error is also carried out in terms of this distance. As a result, the translation and rotation are compensated in this framework simultaneously. In fact, this is the contribution with respect to previous work that addressed only translation or translation and rotation but separately. The new technique has been implemented and tested on a real vehicle. The experiments illustrate how it is more robust and accurate than prior techniques. At the end of the paper, we give an extension of our distance measure to 3D range-data matching problems. I.