Cooperative Localization System Research Articles

Resilient Cooperative Localization Based on Factor Graphs for Multirobot Systems

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.

Cooperative localization for master–salve multi-AUVs based on range measurements

Underwater localization has consistently remained a prominent technical challenge for autonomous underwater vehicles (AUVs). The advent of cooperative localization techniques has emerged as a novel avenue for enhancing localization accuracy. The master–slave cooperative localization mode has gained widespread adoption due to its cost-effectiveness in implementation. In view of the complexity of underwater noise characteristics, in the multi-AUVs cooperative localization system, this paper addresses scenarios involving distance-dependent noise in a master–slave-based multi-AUVs cooperative localization system. To tackle the negative impact of distance-dependent noise and the non-linearity of the distance function, a two-step algorithm is proposed that combines maximum likelihood estimation and the Gaussian belief propagation algorithm (ML-GBP) to estimate the positions of AUVs. The maximum likelihood estimation is employed to cope with the interference caused by distance-dependent noise, and subsequently, the Gaussian belief propagation algorithm, based on range observations and reference information, is used to achieve accurate estimation of AUV positions and implement position correction. Simulation results demonstrate that the proposed ML-GBP algorithm outperforms traditional extended Kalman filter (EKF) and nonparametric belief propagation (NBP) methods by enhancing the localization accuracy of the system while exhibiting superior performance in terms of computational complexity and system communication overhead.

Range-Aided Drift-Free Cooperative Localization and Consistent Reconstruction of Multi-Ground Robots

Fast and flexible dense reconstruction has been extensively studied due to its wide application. Considering the efficiency of multi-robot systems, cooperative reconstruction is gaining attention. Classic methods rely on inter-robot loop closures, which cannot work when there is no common area between robots. Ultra-wideband sensors provide distance measurement and can replace loop closures in multi-robot systems. However, ranging-based schemes do not solve the inconsistency of reconstruction, which is caused by odometry drift. We propose a range-aided cooperative localization and consistent reconstruction system. First, the system's front end tightly couples visual odometry and ranging to perform loop-independent cooperative reconstruction and reduce odometry drift. Second, the back end detects overlapping regions in submaps and proposes a novel dense pose graph optimization (PGO) step to further eliminate all trajectory drift and achieve consistent reconstruction. Extensive field experiments demonstrate the effectiveness and high performance of the system.

Cooperative Localization of Firefighters Based on Relative Ranging Constraints of UWB and Autonomous Navigation

There are many demands for the cooperative localization (CL) of multiple people, such as firefighter rescue. The classical foot-mounted inertial navigation based on zero velocity update (ZUPT) suffers from accumulating error due to the low-cost inertial sensor, and the pre-placed anchors in the ultra-wideband (UWB) system limit the application in an unknown environment. In this study, a group of sensors including the inertial measurement unit (IMU), magnetometer, barometer, and UWB sensor is used. Through the different characteristics of sensors and the position relationship between people, a cooperative localization system using an extended Kalman filter for three-dimensional firefighter tracking is proposed. Ranging information between firefighters from UWB is utilized, and couplings introduced by relative measurement are estimated. Two experiments are designed to verify the proposed algorithm in building and forest environments. Compared with the results of single-person inertial navigation, the average positioning precision of the algorithm in the building and forest is, respectively, improved by 38.93% and 79.01%. This approach successfully suppresses the divergence of positioning errors, and fixed UWB anchors are not needed.

Optimal topology design of multi-target AUVs for 3D cooperative localization formation based on angle of arrival measurement

With the expansion and deepening of ocean research, cooperative localization technology of multi-AUVs is being explored more and more. However, the underwater cooperative localization based on Angle of Arrival (AOA) as well as cooperative localization formation are still relatively less explored research areas. Moreover, the cooperative localization formation has significant impact on the localization accuracy of cooperative localization system. In this paper, firstly, a novel optimization model of multi-target AUVs for 3D cooperative localization formation based on AOA measurement is formulated. Secondly, AOA-based evaluation functions of single and multiple targets are derived for underwater 3D cooperative localization system using the D-optimality criterion. Finally, considering the practical constraints of the underwater environment and the computational complexity of the multi-target system, a Sparrow Search Algorithm based on Simulated Annealing (SA-SSA) is proposed to obtain the optimal formation of the cooperative localization system. The algorithm ensured that the global optimal solution for the cooperative localization system is independent of the initial positions of the leader AUVs. Various simulation examples verify the effectiveness of the results and algorithms. Comparison of proposed algorithm with the traditional iterative algorithm proves that the positioning accuracy and system stability are effectively improved under proposed SA-SSA algorithm.

Decision Tree based diagnosis for hybrid model-based/data-driven fault detection and exclusion of a decentralized multi-vehicle cooperative localization system*

Cooperative navigation systems are one of the main topics of interest in multi-robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (DJS) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data from three Turtlebot3 burger robot equipped with wheel encoders (for prediction), a gyroscope (for the yaw angle) and a Marvelmind (for the absolute position). A ground truth is also recorded using optitrack system.

A Cooperative UAV Swarm Localization Algorithm Based on Probabilistic Data Association for Visual Measurement

A cooperative unmanned aerial vehicle (UAV) swarm has been proven to be a fast and efficient system for performing tasks. The relative measurements based on visual detection will cause data association ambiguity in a swarm with homogeneous visual appearance. To solve this problem, this article introduces a UAV swarm localization system based on probabilistic data association (PDA) for vision-based relative measurements. First, a cooperative localization system is designed that each UAV in the swarm shares its information with other UAVs to help the UAV without global navigation satellite system (GNSS) achieve robust localization. The effect of the position uncertainty of assisted UAVs on the localization performance is considered in the process of visual measurement. Then, this work uses the modified directional joint PDA (MDJPDA) algorithm to solve the problem of data association for visual measurement in the homogeneous UAV swarm. By adding the directional weighting factor, the localization errors caused by the visual observation angle are reduced. Finally, the simulation and experimental results demonstrate that the proposed algorithm is superior to the extended Kalman filter (EKF) algorithm, the modified PDA (MPDA) algorithm, and the modified joint PDA (MJPDA) algorithm in terms of localization accuracy and robustness.

Multimobile Robot Cooperative Localization Using Ultrawideband Sensor and GPU Acceleration

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

LEADER-FOLLOWER COOPERATIVE LOCALIZATION BASED ON VIO / UWB LOOSE COUPLING FOR AGV GROUP

Abstract. In this paper, we propose a Leader-Follower cooperative localization system for AGV group, which equipped with camera/IMU/UWB anchors as the Leader and IMU/UWB tags as the Follower. Firstly, we introduce the plane constraint with SE(2) into VIO to suppress drift in 2D motion; Secondly, the Follower receives the Leader's VIO pose and calculates the relative pose through the multiple groups of UWB ranging. The algorithm of Leader and Follower is decoupled, which means Leader can choose different self_localization technology to adapt to different scenario, such as LiDAR-Inertial Odometry(LIO), GNSS, etc. In the Follower, a tightly coupled graph optimization based algorithm to fuse UWB ranging and IMU is proposed to estimate its relative pose to the Leader. Lastly, extensive field experiment shows the system can realize omni-directional cooperative positioning in indoor and outdoor environments and the accuracy of collaborative positioning reaches centimeter level.

Multi-UAV Cooperative Localization for Marine Targets Based on Weighted Subspace Fitting in SAGIN Environment

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization.

A Novel ANFIS-AQPSO-GA-Based Online Correction Measurement Method for Cooperative Localization

To solve the effect of unreliable acoustic range measurements in cooperative localization of autonomous underwater vehicles, a novel localization method is proposed by using a hybrid metaheuristic algorithm that consists of the adaptive quantum-behaved particle swarm optimization (AQPSO) algorithm combined with the genetic algorithm (GA) to train the adaptive neurofuzzy inference system (ANFIS) model. The method can predict the acoustic ranging errors online, then correct the measurements, and improve the accuracy and stability of the cooperative localization system. We first tested the optimization ability of AQPSO-GA with an open-source dataset, and then, a large number of simulation experiments are performed with actual data collected in lake-water trials to fully compare and analyze the performance of various cooperative localization methods. The experimental results show that, compared with the cooperative localization method based on the cubature Kalman filter, the proposed method can reduce the localization error by 90% and effectively guarantee the stability of the cooperative localization system.

Research on Vehicle-Road Co-Location Method Oriented to Network Slicing Service and Traffic Video

The development of 5G network slicing technology, combined with the application scenarios of vehicle–road collaborative positioning, provides end-to-end, large-bandwidth, low-latency, and highly reliable flexible customized services for Internet of Vehicle (IoV) services in different business scenarios. Starting from the needs of the network in the business scenario oriented to co-location, we researched the application of 5G network slicing technology in the vehicle–road cooperative localization system. We considered scheduling 5G slice resources. Creating slices to ensure the safety of the system, provided an optimized solution for the application of the vehicle–road coordinated positioning system. On this basis, this paper proposes a vehicle–road coordinated combined positioning method based on Beidou. On the basis of Beidou positioning and track estimation, using the advantages of the volumetric Kalman model, a combined positioning algorithm based on CKF was established. In order to further improve the positioning accuracy, vehicle characteristics could be extracted based on the traffic monitoring video stream to optimize the service-oriented positioning system. Considering that the vehicles in the urban traffic system can theoretically only travel on the road, the plan can be further optimized based on the road network information. It was preliminarily verified by simulation that this research idea has improved the relative single positioning method.

Optimal Formation Configuration Analysis for Cooperative Localization System of Multi-AUV

Aiming at the problem that the quality of measurement information directly affects the performance of cooperative localization of multiple autonomous underwater vehicles (multi-AUV), a design method for the optimal formation configuration of multi-AUV is proposed to select the best measurement information. First, for the two cases of whether the measurement information is correlated with the measurement error, the performance evaluation functions of formation configuration of multi-AUV are established based on the information ellipse area. Second, through in-depth analysis of these functions, the design methods of the optimal formation configuration in two cases are proposed respectively. Subsequently, taking the multi-AUV cooperative localization systems with four-master and five-master as examples, based on the proposed design method, the optimal formation configuration for cooperative localization system of multi-AUV in two cases is studied deeply, and the results show that it is dynamic. Besides, a special formation configuration is proposed for the cooperative localization system of multi-AUV, which is easier to be implemented compared with the optimal formation configuration when the localization performance is almost the same. At last, the simulation experiments verify the correctness of the proposed method and the effectiveness of the proposed special formation configuration.

A Novel Measurement Information Anomaly Detection Method for Cooperative Localization

To deal with the influence of abnormal underwater acoustic ranging errors in cooperative localization of autonomous underwater vehicles, a novel measurement information anomaly detection method based on adaptive neuro-fuzzy inference system (ANFIS) is proposed. The method can accurately identify and isolate acoustic distance information with errors exceeding the threshold range even when multiple distance information is alternately used as the measurement data for the filtering algorithm. The adaptive cubature Kalman filter is used to extract the characteristic information, which can better reflect the change of measurement information. According to the preset state threshold, the flag bit obeying Bernoulli distribution is obtained, and the hybrid database is established. This method combines the online data training mechanism with the ANFIS-based detection system to update the ANFIS rules online, which can effectively improve the reliability and accuracy of anomaly detection method, especially when the sample data are insufficient. Experimental results based on data obtained from the actual lake-water trial show that the method can accurately identify the abnormal acoustic distance information and retain the accurate distance information to ensure the stable operation of cooperative localization system.

Improved Maximum Correntropy Cubature Kalman Filter for Cooperative Localization

In this paper, an improved maximum correntropy cubature kalman filter(IMCCKF) is proposed to address the measurement outliers in cooperative localization(CL) of autonomous underwater vehicles (AUVs). The estimated performance of the maximum correntropy cubature kalman filter(MCCKF) algorithm is affected by the kernel bandwidth(KB). The selection value of the KB cannot be determined only by experience in practical CL of AUVs, which will greatly reduce the practical application value of the MCCKF algorithm. The adaptive factor is constructed by comparing the trace size of innovation matrix and the trace size of quantity prediction error matrix, and the KB in the MCCKF is adjusted online by the adaptive factor. Finally, the validity of the proposed IMCCKF method is verified by the lake test data. The experimental results show that the proposed method has the ability to adjust the KB in real time and quickly obtain the optimal value of the KB, and the IMCCKF algorithm can effectively improve the positioning performance of CL system with measurement outliers.

A Novel Cooperative Localization Method Based on IMU and UWB.

In this paper, a range-based cooperative localization method is proposed for multiple platforms of various structures. The localization system of an independent platform might degrade or fail due to various reasons such as GPS signal-loss, inertial measurement unit (IMU) accumulative errors, or emergency reboot. It is a promising approach to solve this problem by using information from neighboring platforms, thus forming a cooperative localization network that can improve the navigational robustness of each platform. Typical ranging-based ultra-wideband (UWB) cooperative localization systems require at least three auxiliary nodes to estimate the pose of the target node, which is often hard to meet especially in outdoor environment. In this work, we propose a novel IMU/UWB-based cooperative localization solution, which requires a minimum number of auxiliary nodes that is down to 1. An Adaptive Ant Colony Optimization Particle Filter (AACOPF) algorithm is customized to integrate the dead reckoning (DR) system and auxiliary nodes information with no prior information required, resulting in accurate pose estimation, while to our knowledge the azimuth have not been estimated in cooperative localization for the insufficient observation of the system. We have given the condition when azimuth and localization are solvable by analysis and by experiment. The feasibility of the proposed approach is evaluated through two filed experiments: car-to-trolley and car-to-pedestrian cooperative localization. The comparison results also demonstrate that ACOPF-based integration is better than other filter-based methods such as Extended Kalman Filter (EKF) and traditional Particle Filter (PF).

Robust Power Allocation for Cooperative Localization in Jammed Wireless Sensor Networks

In this paper, we propose robust power allocation strategies to improve the localization performance in cooperative wireless sensor localization systems when suffering interference of jammer nodes. In wireless sensor localization systems, transmitting power strategies will affect the localization accuracy and determine the lifetime of wireless sensor networks. At the same time, the power allocation problem will be evolution to a new challenge when there are jammed nodes. So in this paper, we first present the optimization framework in jammed cooperative localization systems. Moreover, the imperfect parameter estimations of agent and jammer nodes are considered to develop robust power allocation strategies. In particular, this problem can be transformed into second-order cone programs (SOCPs) to obtain the end solution. Numerical results show the proposed power allocation strategies can achieve better performance than uniform power allocation and the robust schemes can ensure lower localization error than nonrobust power control when systems are subject to uncertainty.

Optimal Sensor Formation for 3D Cooperative Localization of AUVs Using Time Difference of Arrival (TDOA) Method.

The cooperative localization of submerged autonomous underwater vehicles (AUVs) using the Time Difference of Arrival (TDOA) measurements of surface AUV sensors is an effective method for many applications of AUVs. Proper positioning of the sensors to maximize the observability of the AUVs is very critical for cooperative localization. In this paper, a novel method for obtaining the optimal formation of sensor AUVs has been presented for the three-dimensional (3D) cooperative localization of targets using the TDOA technique. An evaluation function for estimating the optimal formation has been derived based on Fisher Information Matrix (FIM) theory for a single target as well as multiple-target cooperative localization systems. An iterative stepping algorithm has been followed to solve the evaluation function and obtain the optimal positions of the sensors. The algorithm ensured that the computation complexity should remain limited, even when the number of sensor AUVs is increased. Various simulation examples are then presented to calculate the optimal formation for different systems/situations. The effect of the position of the reference sensor and operating depth of the target AUVs on the optimal formation of the sensors has also been studied, and conclusions are drawn. For implementation of the proposed method for more practical scenarios, a simulation example is also presented for cases when the target’s position is only known with uncertainty.

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.

Simple solution to the optimal deployment of cooperative nodes in two-dimensional TOA-based and AOA-based localization system

Cellular-based cooperative communication is a promising technique that allows cooperation among mobile devices not only to increase data throughput but also to improve localization services. For a certain number of cooperative nodes, the geometry plays a significant role in enhancing the accuracy of target. In this paper, a simple solution to the deployment of cooperative nodes aiming at the lowest geometric dilution of precision (GDOP) is proposed suitable for both time of arrival-based cooperative localization system and angle of arrival-based cooperative localization system. Inertia dependence factor is suggested to reveal the relationship among the optimal positions of each cooperative node, which extracts the inertia and the recursiveness of deployment. It is shown in simulations that the proposed solution almost achieves the same GDOP as that of global exclusive method but with less complexity.