Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets

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

Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166