The ANSER Project: Data Fusion Across Multiple Uninhabited Air Vehicles

Sukkarieh, Salah; Nettleton, Eric; Kim, Jong Hyuk; Ridley, Matthew; Göktoğan, Ali Haydar; Durrant‐Whyte, Hugh

doi:10.1177/02783649030227005

Cited by 62 publications

(4 citation statements)

References 16 publications

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“…The radius forming the feature vectors of the SR-tree, however, would need to be adaptive in order to generalize to different environments. Estimates of common landmarks are determined separately by a collapsing operation performed on matched landmarks in information form (see Grime and Durrant-Whyte [8], as well as Sukkarieh et al [9], for further reading on fusion using information filtering). Julier and Uhlmann [10] introduced the covariance intersection algorithm as an approach to the data fusion problem.…”

Section: A Related Workmentioning

confidence: 99%

Maximum Likelihood Fusion of Stochastic Maps

Jones

Campbell

Tong

2014

IEEE Trans. Signal Process.

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The fusion of independently obtained stochastic maps by collaborating mobile agents is considered. The proposed approach includes two parts: matching of stochastic maps and maximum likelihood alignment. In particular, an affine invariant hypergraph is constructed for each stochastic map, and a bipartite matching via a linear program is used to establish landmark correspondence between stochastic maps. A maximum likelihood alignment procedure is proposed to determine rotation and translation between common landmarks in order to construct a global map within a common frame of reference. A main feature of the proposed approach is its scalability with respect to the number of landmarks: the matching step has polynomial complexity and the maximum likelihood alignment is obtained in closed form. Experimental validation of the proposed fusion approach is performed using the Victoria Park benchmark dataset.Comment: 10 pages, 8 figures, submitted to IEEE Transactions on Signal Processing on 24-March-201

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Section: A Related Workmentioning

confidence: 99%

Maximum Likelihood Fusion of Stochastic Maps

Jones

Campbell

Tong

2014

IEEE Trans. Signal Process.

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“…The works [18,46,36] demonstrate that, for the case of static states (for instance, in mapping applications, when estimating the location of a set of static objects), the decentralised implementation of the IF allows a local estimation that is the same as the one obtained by a centralised IF with access to all the information (provided that sufficient information is exchanged). In the case of dynamic states, for instance in tracking applications (like the one considered here), it was noticed in [42,1] that if only an estimation about the last state is exchanged between the decentralised nodes, information will be missed with respect to a centralised node.…”

Section: Bayesian Decentralised Data Fusionmentioning

confidence: 99%

Data fusion in ubiquitous networked robot systems for urban services

et al. 2012

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There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks; and wireless communications, like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a Wireless Sensor Network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralised data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 non-overlapping cameras, WSN and several robots.

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“…For instance, UAVs are used to deploy and repair nodes of a wireless sensor network in [27,28]. Besides, the ANSER Project [29] merges autonomous aerial and ground sensors to perform decentralized data fusion and simultaneous localization and mapping (SLAM).…”

Section: Introductionmentioning

confidence: 99%

Ten Years of Cooperation Between Mobile Robots and Sensor Networks

Capitán

Martinez-de-Dios

Maza

et al. 2015

International Journal of Advanced Robotic Systems

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This paper presents an overview of the work carried out by the Group of Robotics, Vision and Control (GRVC) at the University of Seville on the cooperation between mobile robots and sensor networks. The GRVC, led by Professor Anibal Ollero, has been working over the last ten years on techniques where robots and sensor networks exploit synergies and collaborate tightly, developing numerous research projects on the topic. In this paper, based on our research, we introduce what we consider some relevant challenges when combining sensor networks with mobile robots. Then, we describe our developed techniques and main results for these challenges. In particular, the paper focuses on autonomous self-deployment of sensor networks; cooperative localization and tracking; self-localization and mapping; and large-scale scenarios. Extensive experimental results and lessons learnt are also discussed in the paper.

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The ANSER Project: Data Fusion Across Multiple Uninhabited Air Vehicles

Cited by 62 publications

References 16 publications

Maximum Likelihood Fusion of Stochastic Maps

Maximum Likelihood Fusion of Stochastic Maps

Data fusion in ubiquitous networked robot systems for urban services

Ten Years of Cooperation Between Mobile Robots and Sensor Networks

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