Abstract — Range-Only SLAM represents a difficult problem due to the inherent ambiguity of localizing either the robot or the beacons from distance measurements only. Most previous approaches to this problem employ non-probabilistic batch optimizations or delay the initialization of new beacons within a probabilistic filter until a good estimate is available. The contribution of this work is the formulation of RO-SLAM as an online Bayesian estimation process based on a Rao-Blackwellized Particle Filter. The conditional distribution for each beacon is initialized using an additional particle filter which, eventually, is transformed into an extended Kalman filter when the uncertainty becomes sufficiently small. This approach allows the introduction of new beacons without either delay or any special non-probabilistic processing. We validate our proposal with experiments for both simulated and real datasets. I.

With recent advances in battery capacity and the development of hydrogen fuel cells, autonomous underwater vehicles (AUVs) are being used to undertake longer missions that were previously performed by manned or tethered vehicles. As a result, more advanced navigation systems are needed to maintain an accurate position over a larger operational area. The accuracy of the navigation system is critical to the quality of the data collected during survey missions and the recovery of the AUV. Many different methods for navigation in different underwater environments have been proposed in the literature. In this paper, the state of the art in navigation technologies for AUVs is investigated for theoretical and operational systems. Their suitability for use in different environments is compared and current limitations of these methods are identified. In addition, new approaches to address these current problems and areas for future research are suggested. Finally, it is concluded that only geophysically referenced methods will enable AUVs to navigate accurately over large areas and that advances in underwater feature recognition are required before these methods can be implemented in operational AUVs.

SUMMARY This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system. key words: robot localization, RFID, Support Vector Machine, landmark arrangement

Abstract — This paper presents an on-line nonlinear least squares algorithm for multi-sensor autonomous underwater vehicle (AUV) navigation. The approach integrates the global constraints of range to and GPS position of a surface vehicle or buoy communicated via acoustic modems and relative pose constraints arising from targets detected in side-scan sonar images. The approach utilizes an efficient optimization algorithm, iSAM, which allows for consistent on-line estimation of the entire set of trajectory constraints. The optimized trajectory can then be used to more accurately navigate the AUV, to extend mission duration, and to avoid GPS surfacing. As iSAM provides efficient access to the marginal covariances of previously observed features, automatic data association is greatly simplified — particularly in sparse marine environments. A key feature of our approach is its intended scalability to single surface sensor (a vehicle or buoy) broadcasting its GPS position and simultaneous one-way travel time range (OWTT) to multiple AUVs. We discuss why our approach is scalable as well as robust to modem transmission failure. Results are provided for an ocean experiment using a Hydroid REMUS 100 AUV co-operating with one of two craft: an autonomous surface vehicle (ASV) and a manned support vessel. During these experiments the ranging portion of the algorithm ran online on-board the AUV. Extension of the paradigm to multiple missions via the optimization of successive survey missions (and the resultant sonar mosaics) is also demonstrated.

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Abstract—The main purpose of wireless sensor networks is to provide information about an area of interest. In order to fulfill this task, physical parameters have to be measured by as many sensors as possible to improve the knowledge on the sensed area. In contrast, due to the resource-limited nature of sensor networks, the number of actively participating nodes should be kept to a minimum. This paper investigates the trade-off between the two conflicting requirements with special focus on localization of sensor nodes. A distributed algorithm to select subsets of sensor nodes for localization is analyzed regarding the accuracy of localization. I.

Abstract. To jointly map an unknown environment with a team of autonomous robots is a challenging problem, particularly in large environments, as for example the devastated area after a disaster. Under such conditions standard methods for Simultaneous Localization And Mapping (SLAM) are difficult to apply due to possible misinterpretations of sensor data, leading to erroneous data association for loop closure. We consider the problem of multi-robot range-only SLAM for robot teams by solving the data association problem with wireless sensor nodes that we designed for this purpose. The memory of these nodes is utilized for the exchange of map data between multiple robots, facilitating loop-closures on jointly generated maps. We introduce RSLAM, which is a variant of FastSlam, extended for range-only measurements and the multi-robot case. Maps are generated from robot odometry and range estimates, which are computed from the RSSI (Received Signal Strength Indication). The proposed method has been extensively tested in USARSim, which serves as basis for the Virtual Robots competition at RoboCup, and by real-world experiments with a team of mobile robots. The presented results indicates that the approach is capable of building consistent maps in presence of real sensor noise, as well as to improve mapping results of multiple robots by data sharing. 1

This thesis examines reasoning under uncertainty in distributed systems. Unlike in centralized systems, where the observations reside in a single location, the observations in distributed systems are often scattered across the network. To reason accurately, a networked device often needs to incorporate observations from other nodes and must do so with limited computation and communication even for large problems. The reasoning is further complicated by unstable network conditions, characteristic to many real-world networks: the nodes may fail, communication links may become unreliable, and the entire network may get fragmented into several components that cannot communicate with each other. These aspects make distributed inference very challenging. We consider one general problem of distributed filtering for estimating the state of a dynamical system and three independent applications: simultaneous localization and tracking, where a camera network localizes itself by observing a moving object, internal localization of large-scale modular robots, where a robot determines the relative poses of its internal parts,

This paper describes the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is provide a quantitative performance comparison of three estimators: particle filtering (PF), Nonlinear Least Squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance. I.

This paper describes an algorithm for distributed acoustic navigation for Autonomous Underwater Vehicles (AUVs). Whereas typical AUV navigation systems utilize pre-calibrated arrays of static transponders, our work seeks to create a fully mobile network of AUVs that perform acoustic ranging and data exchange with one another to achieve cooperative positioning for extended duration missions over large areas. The algorithm enumerates possible solutions for the AUV trajectory based on dead-reckoning and range-only measurements provided by acoustic modems that are mounted on each vehicle, and chooses the trajectory via minimization of a cost function based on these constraints. The resulting algorithm is computationally efficient, meets the strict bandwidth requirements of available AUV modems, and has potential to scale well to networks of large numbers of vehicles. The method has undergone extensive experimentation, and results from three different scenarios are reported in this paper, each of which utilizes MIT SCOUT Autonomous Surface Craft (ASC) as convenient platforms for testing. In the first experiment, we utilize three ASCs, each equipped with a Woods Hole acoustic modem, as surrogates for AUVs. In this scenario, two ASCs serve as Communication/Navigation Aids (CNAs) for a third ASC that computes its position based exclusively on GPS positions of the CNAs and acoustic range measurements between platforms. In the second scenario, an undersea glider is used in conjunction with two ASCs serving as CNAs. Finally, in the third experiment, a Bluefin12 AUV serves as the target vehicle. All three experiments demonstrate the successful operation of the technique with real ocean data.