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

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Multisensor-based human detection and tracking for mobile service robots

The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments

Target tracking using laser range finder with occlusion

Mestrado em Engenharia MecânicaEste trabalho apresenta uma técnica para a detecção e seguimento de múltiplos alvos móveis usando um sensor de distâncias laser em situações de forte oclusão. O processo inicia-se com a aplicação de filtros temporais aos dados em bruto de modo a eliminar o ruído do sensor seguindo-se de uma segmentação em várias fases com o objectivo de contornar o problema da oclusão. Os segmentos obtidos representam objectos presentes no ambiente. Para cada segmento um ponto representativo da sua posição no mundo é calculado, este ponto é definido de modo a ser relativamente invariante à rotação e mudança de forma do objecto. Para fazer o seguimento de alvos uma lista de objectos a seguir é mantida, todos os objectos visíveis são associados a objectos desta lista usando técnicas de procura baseadas na previsão do movimento dos objectos. Uma zona de procura de forma elíptica é definida para cada objecto da lista sendo nesta zona que se dará a associação. A previsão do movimento é feita com base em dois modelos de movimento, um de velocidade constante e um de aceleração constante e com aplicação de filtros de Kalman. O algoritmo foi testado em diversas condições reais e mostrou-se robusto e eficaz no seguimento de pessoas mesmo em situações de extensa oclusão. ABSTRACT: In this work a technique for the detection and tracking of multiple moving targets in situations of strong occlusion using a laser rangefinder is presented. The process starts by the application of temporal filters to the raw data in order to remove noise followed by a multi phase segmentation with the goal of overcoming occlusions. The resulting segments represent objects in the environment. For each segment a representative point is defined; this point is calculated to better represent the object while keeping some invariance to rotation and shape changes. In order to perform the tracking, a list of objects to follow is maintained; all visible objects are associated with objects from this list using search techniques based on the predicted motion of objects. A search zone shaped as an ellipse is defined for each object; it is in this zone that the association is preformed. The motion prediction is based in two motion models, one with constant velocity and the other with constant acceleration and in the application of Kalman filters. The algorithm was tested in diverse real conditions and shown to be robust and effective in the tracking of people even in situations of long occlusions