Relative Localization Algorithm Research Articles

DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots

The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.

A distributed relative localization approach for air-ground robot formations with onboard sensing

In a multi-robot system, diversity in the sensing and motion models of robotic entities can improve the overall performance. While such heterogeneous systems offer peculiar advantages in terms of robustness and resiliency, positioning and situational awareness of individual robots in these systems remain a challenge. In this paper, the problem of relative localization in a system composed of a drone and multiple unmanned ground vehicles which are desired to move in formation is addressed. By utilizing a leader–follower formation graph, a distance-based relative localization algorithm based on an extended Kalman filter is proposed for online estimation of the relative positions among the ground vehicles. The necessary conditions to satisfy the observability of the unmeasured states are provided. In the proposed framework, the robots exchange a limited amount of information only and do not rely on an external infrastructure, GPS, or magnetometer. Furthermore, an application of the proposed localization framework integrated to custom formation control schemes is proposed. The performance of the proposed approach is evaluated through a set of simulation and real life experiments, and its advantages and limitations are discussed by means of a comparative study.

Unmanned ground vehicle‐unmanned aerial vehicle relative navigation robust adaptive localization algorithm

AbstractThe unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi‐platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV‐UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV‐UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)‐follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non‐linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV‐UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust‐EKF and Robust‐Adaptive‐EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non‐Gaussian distribution. The results show that under the non‐Gaussian distribution conditions, the accuracy of the Robust‐Adaptive‐EKF algorithm is improved by about two to three times compared with the EKF and Robust‐EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self‐adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

Trajectory-Robust RFID Relative Localization Based on Phase Profile Correlation

Indoor localization systems based on radio frequency identification (RFID) technologies are widely adopted in the Internet of Things (IoT). Many of these applications prefer to obtain the relative position of the target object rather than the absolute position. Existing relative positioning methods are subject to strict trajectory constraints and cannot work under nonstraight trajectories. RFID mobile localization which has high accuracy can also be used for relative positioning, but they require the acquisition of precise moving trajectory coordinates. To overcome these challenges, we propose a trajectory-robust relative localization algorithm (TRL), based on phase profiles’ correlation in this article. The intuition of TRL is that the phase of the backscattered signal has a linear mapping relationship with the distance between the reader and the antenna, so the closer the tag is to each other, the higher the correlation of their phase curves is. We use the dynamic time warping (DTW) algorithm to calculate the correlation between the phase profiles of different tags and achieve relative localization of all the tags by sequentially determining their position intervals in the tag sequence. Compared with the state-of-the-art solutions, TRL gives greater flexibility to antenna movement and the demand for trajectory coordinates, and is it fully robust to trajectory error. A series of experimental results show that TRL has significant advantages over the current state-of-the-art schemes in terms of both accuracy and computation time.

An aerial ammunition ad hoc network collaborative localization algorithm based on relative ranging and velocity measurement in a highly-dynamic topographic structure

In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional (3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling (MDS) was first integrated with a trilateration localization method (TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function (SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for high-speed motion models.

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement.

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

Ultra-Wideband and Odometry-Based Cooperative Relative Localization With Application to Multi-UAV Formation Control.

This puts forth an infrastructure-free cooperative relative localization (RL) for unmanned aerial vehicles (UAVs) in global positioning system (GPS)-denied environments. Instead of estimating relative coordinates with vision-based methods, an onboard ultra-wideband (UWB) ranging and communication (RCM) network is adopted to both sense the inter-UAV distance and exchange information for RL estimation in 2-D spaces. Without any external infrastructures prepositioned, each agent cooperatively performs a consensus-based fusion, which fuses the obtained direct and indirect RL estimates, to generate the relative positions to its neighbors in real time despite the fact that some UAVs may not have direct range measurements to their neighbors. The proposed RL estimation is then applied to formation control. Extensive simulations and real-world flight tests corroborate the merits of the developed RL algorithm.

Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization.

In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.

An Optimization Based Approach for Relative Localization and Relative Tracking Control in Multi-Robot Systems

In this paper, an optimization based method is used for relative localization and relative trajectory tracking control in Multi-Robot Systems (MRS's). In this framework, one or more robots are located and commanded to follow time varying trajectories with respect to another (possibly moving) robot reference frame. Such systems are suitable for a considerable number of applications, e.g. patrolling missions, searching operations, perimeter surveillance, and area coverage. Here, the nonlinear and constrained motion and measurement models in an MRS are incorporated to achieve an accurate state estimation algorithm based on nonlinear Moving Horizon Estimation (MHE) and a tracking control method based on Nonlinear Model Predictive Control (NMPC). In order to fulfill the real-time requirements, a fast and efficient algorithm based on a Real Time Iteration (RTI) scheme and automatic C-code generation, is adopted. Numerical simulations are conducted to: first, compare the performance of MHE against the traditional estimator used for relative localization, i.e. extended Kalman filter (EKF); second, evaluate the utilized relative localization and tracking control algorithm when applied to a team of multiple robots; finally, laboratory experiments are performed, for real-time performance evaluation. The conducted simulations validated the adopted algorithm and the experiments demonstrated its practical applicability.

Indoor pseudolite relative localization algorithm with kalman filter

In the indoor pseudolite positioning system, the effect of the clock offset always leads to a large error in the positioning result. To reduce the effect of the clock offset, a relative localization algorithm for the indoor environment is proposed. In this paper, firstly, the Kalman positioning algorithm is used to obtain the coarse baseline. The model of the indoor relative localization and its operation are shown. Using the Kalman filter, the pseudorange double difference is smoothed by the carrier phase double difference. Then, the accurate baseline and the accurate positioning result of the receiver could be obtained. In order to predict the next state precisely, the Kalman corrected result is replaced by the accurate result. The experimental results show that the proposed algorithm has a good localization accuracy and the positioning error is less than 20 cm.

Advances in Signal Processing for Maritime Applications

The maritime domain continues to be important for our society. Significant investments continue to be made to increase our knowledge about what “happens” underwater, whether at or near the sea surface, within the water column, or at the seabed. The latest geophysical, archaeological, and oceanographical surveys deliver more accurate global knowledge at increased resolutions. Surveillance applications allow dynamic systems to be accurately characterized. Underwater exploration is fundamentally reliant on the effective processing of sensor signal data. All maritime applications face the same difficult operating environment: fading channels, rapidly changing environmental conditions, high noise levels at sensors, sparse coverage of the measurement area, limited reliability of communication channels, and the need for robustness and low energy consumption, just to name a few. There are obvious technical similarities in the signal processing that have been applied to different measurement equipment, and this special issue aims to help foster cross-fertilization between these different application areas. The articles in this special issue cover the following topics: First, underwater acoustics: “Underwater broadband source localization based onmodal filtering and features extraction”, “Simulation of matched field processing localization based on EMD denoising and Karhunen-Loeve expansion in underwater waveguide environment,” “A relative-localization algorithm using incomplete pair-wise distance measurements for underwater applications,” “Acoustic particle detection with the ANTARES detector,” “Masking of time-frequency patterns in applications of passive underwater target detection,” “An underwater acoustic implementation of DFT-spread OFDM,” “Low complexity iterative receiver design for shallow water acoustic channels,” and “Automatic indexing and content analysis of whale recordings and XML representation.” Second, underwater nonacoustics: “Silent localization of underwater sensors using magnetometers”, “Hausdorff-based RC and IESIL combined positioning algorithm for underwater geomagnetic navigation”, and “Realistic subsurface anomaly discrimination using electromagnetic induction and an SVM classifier”. Third, radar: “CFAR detection from non-coherent radar echoes using bayesian theory”, “Artificial neural network-based clutter reduction systems for ship size estimation in maritime radars.” Fourth, optics: “An evaluation of pixel-based methods for the detection of floating objects on the sea surface,” “Statistical real-time model for performance prediction of ship detection from micro-satellite electro-optical imagers,” “Techniques for effective optical noise rejection in amplitude-modulated laser optical radars for underwater three-dimensional imaging,” “Underwater image processing: state of the art of restoration and image enhancement methods,” and “A fully automated method to detect and segment a manufactured object in an underwater color image.”

Small-world-based energy-efficient query strategy for sensor networks

Query processing is one of the most important technologies in sensor networks. To reduce energy consumption of query, a small world based query strategy named Contact-Assisted poweR-efficient Direction-sense-achieved query strategy for Sensor Networks (CardSN) was presented. In CardSN, Contacts act as shortcuts to bring down the average path length of the network; a distributed relative localization algorithm was introduced to achieve a sense of direction. Experimental results show that CardSN has good scalability and can save more energy than ZRP and CAPTURE. Therefore, CardSN is of high-performance and energy-efficiency.

Real Time Relative and Absolute Dynamic Localization of Air–Ground Wireless Sensor Networks

A novel approach for relative and absolute localization of wireless sensor nodes using a potential field method is presented. The main idea of our work is to develop relative and absolute localization algorithms for the position estimate of stationary unattended ground sensor (UGS) nodes using a potential field method. A dynamical model is derived for each sensor node to estimate the relative and absolute position estimates under the influence of a certain fictitious virtual force. In the algorithm the sensor nodes do not move physically, but a virtual motion is carried out to generate optimal position estimates. The convergence of the estimator system to a least squares solution is guaranteed using Lyapunov theory. Separate control algorithms for relative and absolute localization are developed which guarantee the convergence of the position estimates. The relative localization algorithm assumes that distance (i.e. range) measurements between UGS nodes are available and for absolute localization algorithm, uninhabited aerial vehicles (UAV) are available with on board GPS such that they have absolute position information together with range measurement information. In the relative localization algorithm the UGS nodes are localized with respect to an internal co-ordinate frame. In absolute localization the UGS nodes are localized with respect to the known absolute position of UAV in the air---ground network. The effectiveness of the control algorithm is highlighted by the real time implementation results.

Structured Light 기법을 이용한 이동 로봇의 상대 위치 추정 알고리즘 연구

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.