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

Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approac

Cooperative Localization under Limited Connectivity

We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic~experiment.Comment: 9 pages, 5 figure

Joint Localization Based on Split Covariance Intersection on the Lie Group

This paper presents a pose fusion method that accounts for the possible correlations among measurements. The proposed method can handle data fusion problems whose uncertainty has both independent part and dependent part. Different from the existing methods, the uncertainties of the various states or measurements are modeled on the Lie algebra and projected to the manifold through the exponential map, which is more precise than that modeled in the vector space. The dealing of the correlation is based on the theory of covariance intersection, where the independent and dependent parts are split to yield a more consistent result. In this paper, we provide a novel method for correlated pose fusion algorithm on the manifold. Theoretical derivation and analysis are detailed first, and then the experimental results are presented to support the proposed theory. The main contributions are threefold: (1) We provide a theoretical foundation for the split covariance intersection filter performed on the manifold, where the uncertainty is associated on the Lie algebra. (2) The proposed method gives an explicit fusion formalism on SE(3) and SE(2), which covers the most use cases in the field of robotics. (3) We present a localization framework that can work both for single robot and multi-robots systems, where not only the fusion with possible correlation is derived on the manifold, the state evolution and relative pose computation are also performed on the manifold. Experimental results validate its advantage over state-of-the-art methods

Split Covariance Intersection Filter Based Visual Localization With Accurate AprilTag Map For Warehouse Robot Navigation

Accurate and efficient localization with conveniently-established map is the fundamental requirement for mobile robot operation in warehouse environments. An accurate AprilTag map can be conveniently established with the help of LiDAR-based SLAM. It is true that a LiDAR-based system is usually not commercially competitive in contrast with a vision-based system, yet fortunately for warehouse applications, only a single LiDAR-based SLAM system is needed to establish an accurate AprilTag map, whereas a large amount of visual localization systems can share this established AprilTag map for their own operations. Therefore, the cost of a LiDAR-based SLAM system is actually shared by the large amount of visual localization systems, and turns to be acceptable and even negligible for practical warehouse applications. Once an accurate AprilTag map is available, visual localization is realized as recursive estimation that fuses AprilTag measurements (i.e. AprilTag detection results) and robot motion data. AprilTag measurements may be nonlinear partial measurements; this can be handled by the well-known extended Kalman filter (EKF) in the spirit of local linearization. AprilTag measurements tend to have temporal correlation as well; however, this cannot be reasonably handled by the EKF. The split covariance intersection filter (Split CIF) is adopted to handle temporal correlation among AprilTag measurements. The Split CIF (in the spirit of local linearization) can also handle AprilTag nonlinear partial measurements. The Split CIF based visual localization system incorporates a measurement adaptive mechanism to handle outliers in AprilTag measurements and adopts a dynamic initialization mechanism to address the kidnapping problem. A comparative study in real warehouse environments demonstrates the potential and advantage of the Split CIF based visual localization solution

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

A Brief Tutorial On Recursive Estimation With Examples From Intelligent Vehicle Applications (Part I): Basic Spirit And Utilities

Estimation is an indispensable process for an ocean of applications, which are rooted in various domains including engineering, economy, medicine, etc. Recursive estimation is an important type of estimation, especially for online or real-time applications. In this brief tutorial, we will explain the utilities, the basic spirit, and some common methods of recursive estimation, with concrete examples from intelligent vehicle applications