Noisy Range Research Articles

Ranging-Based Localizability Optimization for Mobile Robotic Networks

In robotic networks relying on noisy range measurements between agents for cooperative localization, the achievable positioning accuracy strongly depends on the network geometry. This motivates the problem of planning robot trajectories in such multirobot systems in a way that maintains high localization accuracy. We present potential-based planning methods, where localizability potentials are introduced to characterize the quality of the network geometry for cooperative position estimation. These potentials are based on Cramér Rao lower bounds (CRLB) and provide a theoretical lower bound on the error covariance achievable by any unbiased position estimator. In the process, we establish connections between CRLBs and the theory of graph rigidity, which has been previously used to plan the motion of robotic networks. We develop decentralized deployment algorithms appropriate for large networks, and we use equality-constrained CRLBs to extend the concept of localizability to scenarios where additional information about the relative positions of the ranging sensors is known. We illustrate the resulting robot deployment methodology through simulated examples and an experiment.

Area Coverage Optimization using Networked Mobile Robots with State Estimation

In this paper, we present a solution to the area coverage problem using a team of mobile robots with state estimation. A group of autonomous mobile robots is deployed in a two-dimensional area of interest, for example, where communication among robots is limited or noisy as expected in a real life scenario. Each robot estimates its own state (position and orientation) using noisy range and bearing information received from other robots in its operating range. The area of interest is then divided into multiple sub-area using a voronoi tessellation. Using the classical Lloyd's algorithm, each robot employs distributed action command to move towards the centroid of the voronoi cell that it belongs yielding the maximum coverage of the area. Here we emphasize that the network of autonomous robots deployed in the environment is unknown a priori. A set of computer experiments is conducted to validate the fact that the area coverage is still possible under noisy communication among robots deployed in a two-dimensional area.

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

AbstractLocalisation of machines in harsh Industrial Internet of Things (IIoT) environment is necessary for various applications. Therefore, a novel localisation algorithm is proposed for noisy range measurements in IIoT networks. The position of an unknown machine device in the network is estimated using the relative distances between blind machines (BMs) and anchor machines (AMs). Moreover, a more practical and challenging scenario with the erroneous position of AM is considered, which brings additional uncertainty to the final position estimation. Therefore, the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced. Only those AMs will participate in the localisation process, which increases the accuracy of the final location estimate. Then, the closed‐form expression of the proposed greedy successive anchorization process is derived, which prevents possible local convergence, reduces computation, and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise. The results are compared with the state‐of‐the‐art and verified through numerous simulations.

Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach

As a critical sensor for high-level autonomous vehicles, LiDAR’s limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. However, studies in literature on LiDAR’s performance in harsh conditions are insufficient. In this paper, based on a dataset collected with a popular Near-InfraRed (NIR) ToF LiDAR in a well-controlled artificial fog chamber, we statistically model the LiDAR ranging process in fog conditions through a data-driven approach. Specifically, giving an object at a known distance, our model is able to predict LiDAR measures (range and intensity) under various fog conditions. For a transmitted laser under fog, we first model and predict the minimum visibility required to detect its true range or not. Then, the noisy range and intensity measures are sampled from the probabilistic measurement distributions inferred from the dataset. The performance of the proposed method has been quantitatively and qualitatively evaluated. Experimental results show that our approach can provide a promising performance prediction of the utilized NIR ToF LiDAR under fog, which opens a new gate to the quantitative assessment of adverse weather and contributes to the specification of relevant Operational Domain Designs (ODDs). The developed ROS package is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cavayangtao/lanoising</uri> .

ATU: An Aggregate-Then-Update Diffusion Intelligent Estimation Scheme for Adaptive Networked Systems

For distributed estimation arising in the nonlinear least squares (NLLSs) problems over adaptive networks, where every node has the abilities of data processing and learning, only the incomplete local data are exploited by the traditional noncooperative method, thereby resulting in the degradation on estimation performance. In this article, a cooperative diffusion strategy is proposed by using a Gauss-Newton (GN) method in order to fully utilize the diversity of temporal-spatial data on local updates. The proposed algorithm includes two steps, i.e., aggregate then update (ATU), where the aggregating step collects in real time the global information instead of local information due to the diffusion strategy, and the updating step implements the local GN iteration. The resulting ATU diffusion algorithm is a distributed and cooperative system without any increase on communication cost, as compared with the noncooperative version. Based on the detailed convergence analysis for ATU, which is fundamental to the promotion of this algorithm, the sufficient conditions for convergence are derived and the evidences of faster convergence than the noncooperative version are provided. The simulation results confirm the obtained theoretical derivations by applying the ATU algorithm to an NLLS-based target localization problem and show the cooperation gains in many aspects, such as the convergence rate, steady-state accuracy, and robustness to noisy range, step size, node, and link failures.

Encirclement of Moving Targets Using Noisy Range and Bearing Measurements

This paper presents theoretically justified controllers that use relative range and bearing measurements to steer a team of autonomous vehicles, operating without inertial position information, to circular trajectories around a constant-acceleration, constant-velocity, or stationary target. An extended Kalman filter is used to improve the noisy relative measurements and estimate the velocity of the moving target. These estimated values are used in the control laws to encircle constant-velocity moving targets. Lyapunov techniques are utilized to show that the vehicle will converge to the desired circular formations. Additionally, cooperating vehicles are shown to converge to a circular formation with equal temporal spacing using each vehicle’s estimate of the target’s velocity to define a common reference frame. Numerical simulations validate the efficacy of these control laws.

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach.

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.

A Multiobjective Optimization Tool Chain for 3-D Indoor Beacon Placement Problem

Over the past few decades, finding an optimal spatial configuration of localizing sensors has been mainly approached as a single-objective optimization (SOO) problem with a 2-D perspective for indoor designs. This article presents a novel multiobjective optimization (MOO) approach that analyzes the beacon placement problem (BPP) for the three-dimensional coordinate point cloud representation of indoor environments. The present research targets wireless localization scenarios with static obstacles and noisy range measurements. The proposed methodology is an optimization tool chain that explores a set of Pareto-optimal beacon configurations using the nondominated sorting genetic algorithm (NSGA)-II. The derived nondominant solutions are analyzed by simulations for their performance for accuracy and coverage over different indoor designs. Finally, a performance-based ranking system is presented to direct users in achieving a single optimal solution.

UAV-Assisted Wireless Localization for Search and Rescue

In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue. This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs). The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT). The one-hop neighbor information corresponds to range measurements, which may or may not contain noise. For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes. The lower limits on the various performance aspects of this joint approach are presented. For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors. These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9. For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors. To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering. The proposed method is able to achieve linear scaling of the area searched with respect to the noise level. For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%.

TDOA based localization and its application to the initialization of LiDAR based autonomous robots

This work considers the problem of locating a single robot given a set of squared noisy range difference measurements to a set of points (anchors) whose positions are known. In the sequel, localization problem is solved in the Least-Squares (LS) sense by writing the robot position in polar/spherical coordinates. This representation transforms the original nonconvex/multimodal cost function into the quotient of two quadratic forms, whose constrained maximization is more tractable than the original problem. Simulation results indicate that the proposed method has similar accuracy to state-of-the-art optimization-based localization algorithms in its class, and the simple algorithmic structure and computational efficiency makes it appealing for applications with strong computational constraints. Additionally, location information is used to find the initial orientation of the robot with respect to the previously obtained map in scan matching. Thus, the crucial problem of the autonomous initialization and localization in robotics is solved.

Multicomponent positron–electron annihilation kinetics in the MgO–Al2O3 ceramics in the frequency domain

Multicomponent positron–electron annihilation kinetics in initially dried (after natural water adsorption and before water treated process) and with following immersion into water MgO–Al2O3 ceramics sintered at different temperatures (1100 ÷ 1400 °C) has been calculated and analyzed in the frequency domain. The spectra of real (in-phase) $$\chi_{1} \left( \omega \right)$$ and imaginary (quadrature) $$\chi_{2} \left( \omega \right)$$ components of modulated positron–electron annihilation response have been obtained numerically from temporal kinetic characteristics using integral Fourier transform. To calculate Fourier transform combined with spline cubic interpolation for short time range (below 5.5 ns) and piecewise linear interpolation for highly noisy longer times range, domain kinetics have been used. A special method has been proposed to obtain clear frequency response of low-intensive long time components of PEA kinetics, which are almost invisible on spectra because of dominating high-frequency Debye-type component. Strong high-frequency dispersion apparently consists of two Debye-type components with close characteristic frequencies, that leads to widening of large maximum. Low-frequency decay component with characteristic time of about 70 ns shows stronger dependence on sintering temperature of the ceramics and is characterized by significant deviation from Debye form of relaxation. This indicates the formation of some changing with time structures around positrons localized on long time traps.

Autonomous Surface Vehicle Multistep Look-Ahead Measurement Location Planning for Optimal Localization of Underwater Acoustic Transponders

An underwater vehicle may utilize underwater transponders (UTs) for navigation in the absence of global navigation satellite system signals. However, the position of each UT must be known by the underwater vehicle. The problem of an autonomous surface vehicle (ASV) optimally planning measurement locations to localize a set of arbitrarily predeployed acoustic UTs is considered. The ASV is assumed to make noisy range measurements to the UTs. A maximum a posteriori estimator is derived to localize the UTs. In addition, a multistep look-ahead (MSLA) ASV optimal measurement location planning (OMLP) strategy is developed. This planning strategy prescribes future multistep measurement locations. A physical interpretation of the proposed planner in the single-step, single transponder case is provided. Simulation results are presented demonstrating the tradeoff between expected localization performance and computational time associated with various look-ahead horizons and travel distances. Experimental results are given illustrating the proposed MSLA OMLP strategy's performance in environments containing one and two UTs. The proposed OMLP strategy is able to localize UTs to within 4 m of their true locations. Additionally, increasing the planning horizon is demonstrated to yield better UT localization at the cost of increased computational burden.

Adaptive Range-Based Target Localization Using Diffusion Gauss–Newton Method in Industrial Environments

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment. The effectiveness of energy–accuracy tradeoff is also validated.

Learning-Aided 3-D Occupancy Mapping With Bayesian Generalized Kernel Inference

In this paper, we consider the problem of building descriptive three-dimensional (3-D) maps from sparse and noisy range sensor data. We expand our previously proposed method leveraging Bayesian kernel inference for prediction of occupancy in locations not directly observed by a range sensor. In this paper, we show that our kernel inference approach generalizes previous “counting sensor model” approaches from discrete occupancy grids to continuous maps. Our approach enables prediction about occupancy in regions unobserved by the range sensor based on local measurements, and smoothly transitions to a prior in regions lacking sufficient data for reliable inference. Furthermore, we demonstrate quantitatively using simulated data that the mapping performance of our method can be improved by considering rays as continuous observations, rather than sampling discrete free-space point observations along rays. Though the maps produced by our method are in principle continuous, discretizing space affords us several computational advantages, including the ability to apply recursive Bayesian updates, that allow us to perform inference very efficiently, even on large datasets. To demonstrate this advantage, we present experimental results applying this method to large-scale lidar data collected with a ground robot, showing real-time performance. Other field robotics applications, including underwater 3-D mapping with sonar, are explored qualitatively.

FLORIS and CLORIS: Hybrid source and network localization based on ranges and video

Abstract We propose hybrid methods for localization in wireless sensor networks fusing noisy range measurements with angular information (extracted from video). Compared with conventional methods that rely on a single sensed variable, this may pave the way for improved localization accuracy and robustness. We address both the single-source and network (i.e., cooperative multiple-source) localization paradigms, solving them via optimization of a convex surrogate. The formulations for hybrid localization are unified in the sense that we propose a single nonlinear least-squares cost function, fusing both angular and range measurements. We then relax the problem to obtain an estimate of the optimal positions. This contrasts with other hybrid approaches that alternate the execution of localization algorithms for each type of measurement separately, to progressively refine the position estimates. Single-source localization uses a semidefinite relaxation to obtain a one-shot matrix solution from which the source position is derived through factorization. Network localization uses a different approach where sensor coordinates are retained as optimization variables, and the relaxed cost function is efficiently minimized using fast iterations based on Nesterov’s optimal method. Further, an automated calibration procedure is developed to express range and angular information, obtained through different devices, possibly deployed at different locations, in a single consistent coordinate system. This drastically reduces the need for manual calibration that would otherwise negatively impact the practical usability of hybrid range/video localization systems. We develop and test, both in simulation and experimentally, the new hybrid localization algorithms, which not only overcome the limitations of previous fusing approaches, but also compare favourably to state-of-the-art methods, even outperforming them in some scenarios.

Temporally Consistent Road Surface Profile Estimation Using Stereo Vision

Road surface profile (RSP) estimation is an important task to find the imperfections of a road surface, thereby improving ride quality. The RSP estimation has been recently studied using stereo vision owing to its affordable price. However, the existing methods provide noisy and temporally unstable results for real-world driving scenes because of noisy range measurements, noisy pitch angle estimates between the camera and the road surface, and interference from obstacles. This paper proposes a novel method for temporally consistent and robust RSP estimation to overcome these problems. The proposed method consists of three steps: free space estimation, digital elevation map (DEM) estimation, and RSP estimation. We first estimate a drivable area, i.e., free space. For robust and fast free space estimation, we propose an optimization-based non-parametric road surface modeling method and an integral disparity histogram-based free space estimation method. Then, the DEM of the road surface is estimated using range measurements on the free space to avoid obstacle interference. The DEM is temporally updated every frame using the moving average filter and the DEM reference grid update scheme. Owing to these strategies, the proposed method reduces the elevation estimation noise and the pitch angle error, and therefore provides the temporally consistent RSP. We demonstrate the superiority of the proposed method experimentally using stereo image sequences captured in real-world driving scenes.

Distributed Event Localization via Alternating Direction Method of Multipliers

This paper addresses the problem of distributed event localization using noisy range measurements with respect to sensors with known positions. Event localization is fundamental in many wireless sensor network applications such as homeland security, law enforcement, and environmental studies. However, most existing distributed algorithms require the target event to be within the convex hull of the deployed sensors. Based on the alternating direction method of multipliers (ADMM), we propose two scalable distributed algorithms named GS-ADMM and J-ADMM which do not require the target event to be within the convex hull of the deployed sensors. More specifically, the two algorithms can be implemented in a scenario in which the entire sensor network is divided into several clusters with cluster heads collecting measurements within each cluster and exchanging intermediate computation information to achieve localization consistency (consensus) across all clusters. This scenario is important in many applications such as homeland security and law enforcement. Simulation results confirm effectiveness of the proposed algorithms.

LocDyn: Robust Distributed Localization for Mobile Underwater Networks

How do we self-localize large teams of underwater nodes using only noisy range measurements? How do we do it in a distributed way, and incorporating dynamics into the problem? How do we reject outliers and produce trustworthy position estimates? And what if some of the vehicles can measure angular information? The stringent acoustic communication constraints and accuracy needs of our geophysical survey application demand fast and very accurate localization methods. We address dynamic localization as a MAP estimation problem where the prior encodes kinematic information, and we apply a convex relaxation method that takes advantage of previous estimates at each measurement acquisition step. The resulting LocDyn algorithm is fast: It converges at an optimal rate for first order methods. LocDyn is distributed: There is no fusion center responsible for processing acquired data and the same simple computations are performed at each node. LocDyn is accurate: Numerical experiments attest to about 30% smaller positioning error than a comparable Kalman filter. LocDyn is robust: It rejects outlier noise, while benchmarking methods succumb in terms of positioning error.

Efficient local optimisation‐based approach for non‐convex and non‐smooth source localisation problems

This study proposes a novel approach to solve the source localisation problem with noisy range measurements. Since the objective function of the range-based least square problem is non-convex and non-smooth, it is challenging to achieve an accurate estimation. Unlike previous methods that took non-convexity property of the objective function into account, this approach addresses the problem considering the non-smoothness property of the objective function. The problem is solved by a two-stage local optimisation technique which is based on a smooth non-convex approximation of the original objective function. First, the least square method is utilised to obtain a coarse estimation of the problem. Then, the estimation is refined by the trust region method which converges quadratically. Simulation results are presented to show that the proposed approach outperforms other existing methods in terms of the mean squared localisation error and convergence speed.

Range‐spread target detection based on the matched ambiguity function

In this study, the authors deal with the problem of detecting a range-spread target with a high velocity. First, they prove that the mixer output consists of several sinusoidal signals. Then, the matched signal of a noisy mixer output is defined in terms of the energy concentration of the noisy high-resolution range profile (HRRP). Consequently, the matched ambiguity function (MAF) of a noisy mixer output is obtained. Based on the MAF, a range-spread target detector, referred to as MAF-detector (MAF-D), is proposed. The MAF-D uses a single noisy HRRP and thus can detect the target with a high translational velocity and a high rotational velocity. Experimental results, conducted by the recorded radar data, show that the proposed detector outperforms the conventional detectors.