1,721,042 research outputs found
People tracking with a mobile robot: A comparison of Kalman and particle filters
No full textPeople tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system implemented to perform people tracking with a mobile robot using sensor fusion. Finally, we report several experiments where the three filters are compared in terms of accuracy and robustness. In particular we show that, for this kind of applications, the UKF can perform as well as a particle filter but at a much lower computational cost
Multisensor data fusion for joint people tracking and identification with a service robot
No full textPeople tracking and identification with a mobile robot
No full textIn this paper we present a novel and efficient solution for tracking and identifying people with a mobile robot using multisensor data fusion. The system utilizes a laser device to detect human legs and a PTZ camera to find faces, then the relative data is fused with a sequential Unscented Kaiman Filter to perform real-time tracking. A metric based on the Bhattacharyya coefficient for color histogram comparison is also adopted to identify persons wearing different clothes. Finally, integrating the information coming from the tracking and the identification modules, we improve the robustness of the data association process. Some experiments with a mobile robot show the effectiveness of our approach. © 2007 IEEE
Computationally efficient solutions for tracking people with a mobile robot: An experimental evaluation of Bayesian filters
No full textModern 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 neighborhood. 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. © 2009 Springer Science+Business Media, LLC
Multisensor-based human detection and tracking for mobile service robots
No full textOne 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 this 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 leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative 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. © 2008 IEEE
Multimodal perception and recognition of humans with a mobile service robot
No full textMobile service robots are becoming more and more popular, both in public and private places, so their perception capabilities must be adequate to detect and recognize people. One of the methods to accomplish the last task is face recognition, but this is unfortunately very challenging because of the human and robot motion. Also, besides the identities of individuals, the system should be able to distinguish between known and unknown people, and deal with this information accordingly. These challenging problems can be solved with a bank of Bayesian filters that simultaneously track and recognize the person of interest using laser and visual data. The paper extends this solution and proposes an improved version, which combines face recognition with human clothes and height identification, and that can also distinguish unknown people. The effectiveness of the system is demonstrated by several experiments with a mobile service robot. © 2008 IEEE
Witnessing in the ancient Near East. I testimoni nel Vicino Oriente antico.Proceedings of the round table held at the University of Verona - February 15, 2008
No full textA bank of unscented kalman filters for multimodal human perception with mobile service robots
Full text linkA new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics. © Springer Science & Business Media BV 2010
Multimodal robot perception for robust human tracking and recognition
No full textModern service robot must be provided with a fast, reliable system for tracking and recognizing people in order to operate safely and efficiently in human environ- ments. While most of the current approaches consider these two tasks as independent processes, the solution presented in this chapter is based on an original multimodal system that performs simultaneous people tracking and identification, combining dif- ferent sensors to detect humans as well as algorithms to recognize them. The multisensor solution adopted for human detection, based on the robot's laser and camera, is initially introduced. The laser device can detect human legs, while the camera locates frontal faces. Thanks to a robust and efficient histogram comparison, vision is also used to distinguish the clothes of the subjects being tracked. Sensor in- formation is fused within a Bayesian framework to perform joint people tracking and recognition. The solution is based on a bank of filters that integrates all the available observations and generates estimates weighted by identity's probabilities. The infor- mation for human height, clothes and face recognition is stored inside a database of known people. The modularity of the design facilitates the integration of additional perception algorithms (e.g. sound localization, voice recognition, etc.) for possible improvements of the robot' sensing system. The effectiveness of the current approach is demonstrated by several experiments conducted with real mobile robots in presence of people. The successful performance of the proposed solution confirms also its high potential for real world applications of service robotics
On-line inference comparison with Markov logic network engines for activity recognition in AAL environments
No full textWe address possible solutions for a practical application of Markov Logic Networks to online activity recognition, based on domotic sensors, to be used for monitoring elderly with mild cognitive impairments. Our system has to provide responsive information about user activities throughout the day, so different inference engines are tested. We use an abstraction layer to gather information from commercial domotic sensors. Sensor events are stored using a non-relational database. Using this database, evidences are built to query a logic network about current activities. Markov Logic Networks are able to deal with uncertainty while keeping a structured knowledge. This makes them a suitable tool for ambient sensors based inference. However, in their previous application, inferences are usually made offline. Time is a relevant constrain in our system and hence logic networks are designed here accordingly. We compare in this work different engines to model a Markov Logic Network suitable for such circumstances. Results show some insights about how to design a low latency logic network and which kind of solutions should be avoided
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