The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
Many beacon networks provide direct, albeit noisy, measurements of relative range or bearing. For example, acoustic long baseline (LBL) networks are frequently used to localize underwater vehicles @cite_2 @cite_8 , while a number of low-cost systems exist that use RF and ultrasound to measure range @cite_13 @cite_15 . propose an algorithm for estimating location based upon noise-corrupted range measurements, formulating the problem as one of realizing a two-dimensional graph whose structure is consistent with the observed ranges. describe a geometric technique that estimates a robot's location as it navigates a network of fixed beacons using either range or bearing observations. employ spectral methods to localize camera networks using angular measurements, without the need for a global coordinate frame. Alternatively, evaluate the use of feedforward and recurrent neural network architectures to localize a receiver based upon noisy range measurements.
@cite_8 @cite_15 @cite_13 @cite_2
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This paper considers the noisy range-only network localization problem in which measurements of relative distances between agents are used to estimate their positions in networked systems. When distance information is noisy, existence and uniqueness of location solution are usually not guaranteed. It is well known that in presence of distance measurement noise, a node may have discontinuous deformations (e.g., flip ambiguities and discontinuous flex ambiguities). Thus, there are two issues that we consider in the noisy localization problem. The first one is the location estimate error propagated from distance measurement noise. We compare two kinds of analytical location error computation methods by assuming that each distance is corrupted with independent Gaussian random noise. These analytical results help us to understand effects of the measurement noises on the position estimation accuracy. After that, based on multidimensional scaling theory, we propose a distributed localization algorithm to solve the noisy range network localization problem. Our approach is robust to distance measurement noise, and it can be implemented in any random case without considering the network setup constraints. Moreover, a refined version of distributed noisy range localization method is developed, which achieves a good tradeoff between computational effort and global convergence especially in large-scale networks. This paper is about using range-only data to navigate an autonomous underwater vehicle (AUV). We assume the vehicle is equipped with conventional long base line (LBL) transceiver which measures acoustic time of flights (TOFs) between vehicle and small submerged transponders. Using only range data and no prior information other than approximate water column depth, we solve for both transponder location and vehicle trajectory. Results are given using data from a AUV operating in shallow water. A ground truth comparison is made with surveyed transponder locations and trajectory estimates from an on-board Doppler Compass LBL derived navigation filter. Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented. In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization. In this paper, we consider the problem of precisely localizing a group of stationary targets using a single stereo camera mounted on a mobile robot. In particular, assuming that at least one pair of stereo images of the targets is available, we seek to determine where to move the stereo camera so that the localization uncertainty of the targets is minimized. We call this problem the Next-Best-View problem. The advantage of using a stereo camera is that, using triangulation, the two simultaneous images can yield range and bearing measurements of the targets, as well as their uncertainty. We use a Kalman filter to fuse location and uncertainty estimates as more measurements are acquired. Our solution to the Next-Best-View problem is to iteratively minimize the fused uncertainty of the targets' locations subject to field-of-view constraints. We capture these objectives by appropriate artificial potentials on the camera's relative frame and the global frame, respectively. In particular, with every new observation, the mobile stereo camera computes the new next best view on the relative frame and subsequently realizes this view in the global frame via gradient descent on the space of robot positions and orientations, until a new observation is made. Integration of next best view with motion planning results in a hybrid system, which we illustrate in computer simulations. In this paper, we control image collection for a mobile stereo camera that is actively localizing a group of mobile targets. In particular, assuming that at least one pair of stereo images of the targets is available, we propose a novel approach to control the rotation and translation of the stereo camera so that the next observation of the targets will minimize their localization uncertainty. We call this problem the Next-Best-View problem for mobile targets (mNBV). The advantage of using a stereo camera is that, using triangulation, the two simultaneous images taken by the robot during a single observation can yield range and bearing measurements of the targets, as well as their uncertainty. A Kalman filter fuses the full state history and covariance estimates, as more measurements are acquired. Our solution to the mNBV problem determines the relative transformations between camera and targets that will minimize the fused uncertainty of the targets' locations. We determine a motion plan that realizes the mNBV while respecting field of view constraints. In particular, with every new observation, we compute a new mNBV in the frame relative to the camera and subsequently realize this view in global coordinates via a gradient descent algorithm that also respects field of view constraints. Integration of mNBV with motion planning results in a hybrid system, which we illustrate in computer simulations. Most autonomous underwater vehicle (AUV) systems rely on prior knowledge of beacon locations for localization. We present a system capable of navigating without prior beacon locations. Noise and outliers are major issues; we present a powerful outlier rejection method that imposes geometric constraints on measurements. We have successfully applied our algorithm to real-world data and have demonstrated navigation performance comparable to that of systems that assume known beacon locations. Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations. In cooperative navigation, teams of mobile robots obtain range and or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange range measurements using acoustic modems to perform mobile trilateration. While the sharing of information between vehicles can be highly beneficial, exchanging measurements and state estimates can also be dangerous because of the risk of measurements being used by a vehicle more than once; such data re-use leads to inconsistent (overconfident) estimates, making data association and outlier rejection more difficult and divergence more likely. In this paper, we present a technique for the consistent cooperative localization of multiple AUVs performing mobile trilateration. Each AUV establishes a bank of filters, performing careful bookkeeping to track the origins of measurements and prevent the use any of the measurements more than once. The multiple estimates are combined in a consistent manner, yielding conservative covariance estimates. The technique is illustrated using simulation results. The new method is compared side-by-side with a naive approach that does not keep track of the origins of measurements, illustrating that the new method keeps conservative covariance bounds whereas state estimates obtained with the naive approach become overconfident and diverge. The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm s. > An asynchronous position measurement system is proposed for indoor localization in this paper. The demonstrated system consists of a UWB transmitter and several energy detection receivers whose positions are known. The position measurement process starts with the locator emitting a UWB pulse. Upon arrival, the pulse is amplified and retransmitted by the target to be located. Signals from both the locator and the target are captured by the receivers. No synchronization mechanism is implemented. Instead, the proposed system measures the differential TOA between the direct coupling signal of the locator and the target signal. Together with the knowledge of the locator transmitter and receiver positions, the absolute range that the pulse travels can be calculated. The sum of transmitter-target range and target-receiver range defines an ellipse and the target resides on the intersections of several such ellipses. It is shown via the Cramer-Rao lower bound that the absolute-range-based elliptical localization is potentially more accurate than the relative-range-based hyperbolic localization. Our proposed system is able to achieve positioning error bound comparable to synchronous absolute-range-based localization systems while eliminating the cost of synchronization. A mobile robot we have developed is equipped with sensors to measure range to landmarks and can simultaneously localize itself as well as locate the landmarks. This modality is useful in those cases where environmental conditions preclude measurement of bearing (typically done optically) to landmarks. Here we extend the paradigm to consider the case where the landmarks (nodes of a sensor network) are able to measure range to each other. We show how the two capabilities are complimentary in being able to achieve a map of the landmarks and to provide localization for the moving robot. We present recent results with experiments on a robot operating in a randomly arranged network of nodes that can communicate via radio and range to each other using sonar. We find that incorporation of inter-node measurements helps reduce drift in positioning as well as leads to faster convergence of the map of the nodes. We find that addition of a mobile node makes the SLAM feasible in a sparsely connected network of nodes The paper analyzes the properties of the full covariance simultaneous localization and map building problem (SLAM). We prove that, even for the special case of a stationary vehicle (with no process noise) which uses a range-bearing sensor and has non-zero angular uncertainty, the full covariance SLAM algorithm always yields an inconsistent map. We also show, through simulations, that these conclusions appear to extend to a moving vehicle with process noise. However, these inconsistencies only become apparent after several hundred beacon updates. The localization of sensor nodes is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from GPS devices or by virtue of being manually placed. In this paper, we propose a distributed method to localization of sensor nodes using a single moving beacon, where sensor nodes compute their position estimate based on the range-free technique. Two parameters are critical to the location accuracy of sensor nodes: the radio transmission range of the beacon and how often the beacon broadcasts its position. Theoretical analysis shows that these two parameters determine the upper bound of the estimation error when the traverse route of the beacon is a straight line. We extend the position estimate when the traverse route of the beacon is randomly chosen in a real-world situation, where the radio irregularity might cause a node to miss some crucial coordinate information from the beacon. We further point out that the movement pattern of the beacon plays a pivotal role in the localization task for sensors. To minimize estimation errors, sensor nodes can carry out a variety of algorithms in accordance with the movement of the beacon. Simulation results compare variants of the distributed method in a variety of testing environments. Real experiments show that the proposed method is feasible and can estimate the location of sensor nodes accurately, given a single moving beacon. Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable. This paper describes a distributed, linear-time algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a two-dimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available. We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles real-world deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes. Ultra-wideband (UWB) localization is a recent technology that performs competitively with many indoor localization methods currently available. Despite its desirable traits, such as potential high accuracy and high material penetrability, the resolution of non-line-of-sight signals remains a very hard problem and has a significant impact on the localization performance. In this work, we address the peculiarities of UWB error behavior by building models that capture the spatiality as well as the multimodal statistics of the error behavior. Our framework utilizes tessellated maps that associate probabilistic error models to localities in space. In addition to our UWB localization strategy (which provides absolute position estimates), we investigate the effects of collaboration in the form of relative positioning. To this end, we develop a relative range and bearing model and, together with the UWB model, present a unified localization technique based on a particle filter framework. We test our approach experimentally on a group of 10 mobile robots equipped with UWB emitters and extension modules providing inter-robot relative range and bearing measurements. Our experimental insights highlight the benefits of collaboration, which are consistent over numerous experimental scenarios. Also, we show the relevance, in terms of positioning accuracy, of our multimodal UWB measurement model by performing systematic comparisons with two alternative measurement models. Our final results show median localization errors below 10 cm in cluttered environments, using a modest set of 50 particles in our filter. Localization schemes for wireless sensor networks can be classified as range-based or range-free. They differ in the information used for localization. Range-based methods use range measurements, while range-free techniques only use the content of the messages. None of the existing algorithms evaluate both types of information. Most of the localization schemes do not consider mobility. In this paper, a Sequential Monte Carlo Localization Method is introduced that uses both types of information as well as mobility to obtain accurate position estimations, even when high range measurement errors are present in the network and unpredictable movements of the nodes occur. We test our algorithm in various environmental settings and compare it to other known localization algorithms. The simulations show that our algorithm outperforms these known range-oriented and range-free algorithms for both static and dynamic networks. Localization improvements range from 12 to 49 in a wide range of conditions. Presents a technique for relocation of a mobile robot using sonar data. Relocation is the process of determining the position and orientation of a mobile robot with respect to the global reference frame in situations where no a priori estimate of the robot's location is available. A two dimensional model of the environment is employed consisting of planes, corners, edges, and cylinders. A physically based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. The sensor model makes it possible to employ individual range returns as data features in a constraint-based search to determine the robot's position. To increase the efficiency of search, a hypothesize and verify technique is employed in which position of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. Accurate relocation is demonstrated from sparse sonar data in the presence of unmodeled objects.