Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potential to map an environment more quickly and robustly than a single vehicle. This paper presents a cooperative CML algorithm that merges sensor and navigation information from multiple autonomous vehicles. The algorithm presented is based on stochastic estimation and uses a feature-based approach to extract landmarks from the environment. The theoretical framework for the collaborative CML algorithm is presented, and a convergence theorem central to the cooperative CML problem is proved for the rst time. This theorem quanties the performance gains of collaboration, allowing for determination of the number of cooperating vehicles required to accomplish a task. A simulated implementation of the collaborative CML algorithm demonstrates substantial performance improvement over non-cooperative CML.

Abstract. In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle’s motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer. Keywords: laser scanning, navigation, self-localization, mobile robots, 3D modeling, Monte-Carlo localization 1.

This paper describes decoupled stochastic mapping (DSM), a new computationally efficient approach to large-scale concurrent mapping and localization (CML). DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the number of submaps. The approach is demonstrated via simulations and experiments. Simulation results are presented for the case of an autonomous underwater vehicle (AUV) navigating in an unknown environments with 110 and 1200 features using simulated observations of point features by a forward look sonar. Empirical tests are used to examine the consistency of the error bounds calculated by the different methods. Experimental results are also presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 m testing tank.

This paper introduces a new method for representing two-dimensional maps, and shows how this representation may be applied to concurrent localization and mapping problems involving multiple robots. We introduce the notion of a manifold map; this representation takes maps out of the plane and onto a two-dimensional surface embedded in a higher-dimensional space. Compared with standard planar maps, the key advantage of the manifold representation is self-consistency: as we will show, manifold maps do not suffer from the `cross over' problem that planar maps commonly exhibit in environments containing loops. This self-consistency facilitates a number of important autonomous capabilities, including robust retro-traverse, lazy loop closure, active loop closure using robot rendezvous, and, ultimately, autonomous exploration.

This paper describes a real-time implementation of feature-based concurrent mapping and localization (CML) running on a mobile robot in a dynamic indoor environment. Novel characteristics of this work include: (1) a hierarchical representation of uncertain geometric relationships that extends the SPMap framework, (2) use of robust statistics to perform extraction of line segments from laser data in real-time, and (3) the integration of CML with a "roadmap" path planning method for autonomous trajectory execution. These innovations are combined to demonstrate the ability for a mobile robot to autonomously return back to its starting position within a few centimeters of precision, despite the presence of numerous people walking through the environment.

An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot’s relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot’s partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy. 1

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, trash bins).

In recent years, particle filters have solved several hard perceptual problems in robotics. Early successes of particle filters were limited to low-dimensional estimation problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The fact that every model---no mater how detailed---fails to capture the full complexity of even the most simple robotic environments has lead to specific tricks and techniques essential for the success of particle filters in robotic domains. This article surveys some of these recent innovations, and provides pointers to in-depth articles on the use of particle filters in robotics.

Abstract — This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach. I.

In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtaining a set of sensor measurements at each position. The goal is to process the sensor data to produce an estimate of the trajectory of the robot while concurrently building a map of the environment. In this paper, we describe a generalized framework for CML that incorporates temporal as well as spatial correlations. The representation is expanded to incorporate past vehicle positions in the state vector. Estimates of the correlations between current and previous vehicle states are explicitly maintained. This enables the consistent initialization of map features using data from multiple time steps. Updates to the map and the vehicle trajectory can also be performed in batches of data acquired from multiple vantage points. The method is illustrated with sonar data from a testing tank and via experiments with a B21 land mobile robot, demonstrating the ability to perform CML with sparse and ambiguous data. KEY WORDS—mapping, navigation, mobile robots 1.