Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

sUAS Swarm Navigation using Inertial, Range Radios and Partial GNSS

Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e. a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g., precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU, baro-altimeter and range-radio measurements to obtain the platform’s absolute and relative position. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range meas-urements. This solution enables the system to maintain an accurate relative position estimate, and reduce the drift in the swarm’s absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components (three multi-copters and one ground station) and analyzes and discusses the performance using both simulation and sUAS flight test data

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica

Correlated-Data Fusion and Cooperative Aiding in GNSS-Stressed or Denied Environments

University of Minnesota Ph.D. dissertation. September 2014. Major: Aerospace Engineering and Mechanics. Advisor: Demoz Gebre-Egziabher. 1 computer file (PDF); xiii, 172 pages.A growing number of applications require continuous and reliable estimates of position, velocity, and orientation. Price requirements alone disqualify most traditional navigation or tactical-grade sensors and thus navigation systems based on automotive or consumer-grade sensors aided by Global Navigation Satellite Systems (GNSS), like the Global Positioning System (GPS), have gained popularity. The heavy reliance on GPS in these navigation systems is a point of concern and has created interest in alternative or back-up navigation systems to enable robust navigation through GPS-denied or stressed environments. This work takes advantage of current trends for increased sensing capabilities coupled with multilayer connectivity to propose a cooperative navigation-based aiding system as a means to limit dead reckoning error growth in the absence of absolute measurements like GPS. Each vehicle carries a dead reckoning navigation system which is aided by relative measurements, like range, to neighboring vehicles together with information sharing. Detailed architectures and concepts of operation are described for three specific applications: commercial aviation, Unmanned Aerial Vehicles (UAVs), and automotive applications. Both centralized and decentralized implementations of cooperative navigation-based aiding systems are described. The centralized system is based on a single Extended Kalman Filter (EKF). A decentralized implementation suited for applications with very limited communication bandwidth is discussed in detail. The presence of unknown correlation between the a priori state and measurement errors makes the standard Kalman filter unsuitable. Two existing estimators for handling this unknown correlation are Covariance Intersection (CI) and Bounded Covariance Inflation (BCInf) filters. A CI-based decentralized estimator suitable for decentralized cooperative navigation implementation is proposed. A unified derivation is presented for the Kalman filter, CI filter, and BCInf filter measurement update equations. Furthermore, characteristics important to the proper implementation of CI and BCInf in practice are discussed. A new covariance normalization step is proposed as necessary to properly apply CI or BCInf. Lastly, both centralized and decentralized implementations of cooperative aiding are analyzed and evaluated using experimental data in the three applications. In the commercial aviation study aircraft are simulated to use their Automatic Dependent Surveillance - Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) systems to cooperatively aid their on board INS during a 60 min GPS outage in the national airspace. An availability study of cooperative navigation as proposed in this work around representative United States airports is performed. Availabilities between 70-100% were common at major airports like LGA and MSP in a 30 nmi radius around the airport during morning to evening hours. A GPS-denied navigation system for small UAVs based on cooperative information sharing is described. Experimentally collected flight data from 7 small UAV flights are played-back to evaluate the performance of the navigation system. The results show that the most effective of the architectures can lead to 5+ minutes of navigation without GPS maintaining position errors less than 200 m (1-σ). The automotive case study considers 15 minutes of automotive traffic (2,000 + vehicles) driving through a half-mile stretch of highway without access to GPS. Automotive radar coupled with Dedicated Short Range Communication (DSRC) protocol are used to implement cooperative aiding to a low-cost 2-D INS on board each vehicle. The centralized system achieves an order of magnitude reduction in uncertainty by aggressively aiding the INS on board each vehicle. The proposed CI-based decentralized estimator is demonstrated to be conservative and maintain consistency. A quantitative analysis of bandwidth requirements shows that the proposed decentralized estimator falls comfortably within modern connectivity capabilities. A naive implementation of the high-performance centralized estimator is also achievable, but it was demonstrated to be burdensome, nearing the bandwidth limits