Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to map-building. We present a method for computing the global metrical map that builds on the structure provided by a topological map. This allows us to factor the uncertainty in the map into local metrical uncertainty (which is handled well by existing SLAM methods), global topological uncertainty (which is handled well by recently developed topological maplearning methods), and global metrical uncertainty (which can be handled effectively once the other types of uncertainty are factored out). We believe that this method for building the global metrical map will be scalable to very large environments.

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

Abstract — In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s performance in a great variety of tasks including localization, tracking, and map building. In this paper, we present a novel probabilistic measurement model for range finders, called Gaussian beam processes, which treats the measurement modeling task as a nonparametric Bayesian regression problem and solves it using Gaussian processes. The major benefit of our approach is its ability to generalize over entire range scans directly. This way, we can learn the distributions of range measurements for whole regions of the robot’s configuration space from only few recorded or simulated range scans. Especially in approximative approaches to state estimation like particle filtering or histogram filtering, this leads to a better approximation of the true likelihood function. Experiments on real world and synthetic data show that Gaussian beam processes combine the advantages of two popular measurement models. I.

Scan matching is a popular way of recovering a mobile robot's motion and constitutes the basis of many localization and mapping approaches. Consequently, a variety of scan matching algorithms have been proposed in the past. All these algorithms share one common attribute: They match pairs of scans to obtain spatial relations between two robot poses. In this paper we present a method for matching multiple scans simultaneously. We discuss the need for such a method and describe how the result of such a multi-scan matching can be incorporated into relation-based SLAM in the Lu and Milios style.

Abstract — Traditionally, the SLAM problem solves the localization and mapping problem in explored and sensed regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental structure predictor to predict the structure inside an unexplored region (i.e., lookahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can utilize the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be utilized to localize the robot and speed up the SLAM process. We also derive the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM in a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real-time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in an indoor environment. I.

Abstract—Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao–Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments. Index Terms—Bayes procedures, environmental-structure prediction, simultaneous localization and mapping (SLAM). I.

Abstract—Place recognition addresses the problem of determining whether a robot is in a map, and if so, globally localizing, without being given any prior estimate. An efficient method of solving this problem involves selecting a set of keypoints which encode the local region, and then utilizing a sublinear-time nearest neighbors search into a database of keypoints previously generated from the global map to find places with common features. We present an algorithm to embed arbitrary keypoint descriptors in a metric space, which is required in order to frame the problem as a nearest neighbor search. Given that there are a multitude of possibilities for keypoint design, we propose a general methodology for comparing keypoint location selection heuristics and descriptor models that describe the region around the keypoint. With respect to keypoint locations, we introduce a metric that encodes how likely it is that the keypoint will be found in independent mapping passes given the presence of noise and occlusions. Metrics for keypoint descriptors are used to assess the separation between the distributions of matches and non-matches and the probability the correct match will be found in a k-nearest neighbors search. We apply our design evaluation methodology to three keypoint selection heuristics and five keypoint descriptor models. Verification of the test outcomes is done by comparing the various keypoint designs on a kilometers-scale place recognition problem. I.

Abstract — We consider the problem of multi-robot coordination subject to constraints on the configuration. Specifically, we examine the case in which a mobile network of robots must search, survey, or cover an environment while remaining connected. While many algorithms utilize continual connectivity for such tasks, we relax this requirement and introduce the idea of periodic connectivity, where the network must regain connectivity at a fixed interval. We show that, in some cases, this problem reduces to the well-studied NP-hard multi-robot informative path planning (MIPP) problem, and we propose an online algorithm that scales linearly in the number of robots and allows for arbitrary periodic connectivity constraints. We prove theoretical performance guarantees and validate our approach in the coordinated search domain in simulation and in real-world experiments. Our proposed algorithm significantly outperforms a gradient method that requires continual connectivity and performs competitively with a market-based approach, but at a fraction of the computational cost. I.

Abstract — Generating accurate environment representations can significantly improve the autonomy of mobile robots. In this article we present a novel probabilistic technique for solving the full SLAM problem by jointly solving the data registration problem and the accurate reconstruction of the underlying geometry. The key idea of this paper is to incorporate spatial correlation models as prior knowledge on the map we seek to construct. We formulate the mapping problem as a maximum a-posteriori estimation comprising common probabilistic motion and sensor models as well as two spatial correlation models to guide the optimization. Instead of discarding data at an early stage, our algorithm makes use of all data available in the optimization process. When applied to SLAM, our method generates maps that closely resemble the real environment. We compare our approach to state-of-the-art algorithms, using both real and synthetic data sets. I.

Abstract — Localization and mapping are essential capabilities of virtually all mobile robots. These topics have been the focus of a great deal of research, but it is not always easy to tell which methods are best. This paper discusses performance evaluation for an important sub-problem of robot mapping, map optimization. We explore aspects underlying the evaluation of map optimization such as the quality of the result and computational complexity. For each aspect we discuss evaluation metrics and provide specific recommendations. I.