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Autonomous Robots
Erschienen in: Autonomous Robots 3-4/2020

28.01.2019

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

verfasst von: Ruofei Ouyang, Bryan Kian Hsiang Low

Erschienen in: Autonomous Robots | Ausgabe 3-4/2020

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Abstract

This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of large-scale environmental phenomena. To overcome the limitations of scale in existing works, our proposed algorithms allow every mobile sensing agent to utilize a different support set and dynamically switch to another during execution for encapsulating its own data into a local summary that, perhaps surprisingly, can still be assimilated with the other agents’ local summaries (i.e., based on their current support sets) into a globally consistent summary to be used for predicting the phenomenon. To achieve this, we propose a novel transfer learning mechanism for a team of agents capable of sharing and transferring information encapsulated in a summary based on a support set to that utilizing a different support set with some loss that can be theoretically bounded and analyzed. To alleviate the issue of information loss accumulating over multiple instances of transfer learning, we propose a new information sharing mechanism to be incorporated into our algorithms in order to achieve memory-efficient lazy transfer learning. Empirical evaluation on three real-world datasets for up to 128 agents show that our algorithms outperform the state-of-the-art methods.
Vorheriger Artikel Cooperative visual-inertial sensor fusion: fundamental equations and state determination in closed-form
Nächster Artikel SOUL: data sharing for robot swarms

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Fußnoten
1
PITC generalizes the Bayesian Committee Machine (BCM) (Schwaighofer and Tresp 2002), the latter of which assumes the support set to be the set of unobserved input locations whose measurements are to be predicted (Quiñonero-Candela and Rasmussen 2005). As a result, BCM does not scale well with a large set of such unobserved input locations.
 
2
An exception is the work of Park et al. (2011) that overcomes this boundary effect by imposing continuity constraints along the boundaries in a centralized manner.
 
3
The conditional independence of \(Y_{\mathcal {D}_1},\ldots ,Y_{\mathcal {D}_N}\) given \(Y_{\mathcal {S}}\) assumed by PITC and PIC (hence, GP-DDF and GP-\(\hbox {DDF}^+\)) improves their scalability over the GP model (Sect. 2) at the cost of poorer predictive performance. To potentially reduce the degree of violation of this assumption, an informative support set can be \(\hbox {selected}^6\). Furthermore, the experimental results in Chen et al. (2015) show that GP-DDF and GP-\(\hbox {DDF}^+\) can achieve predictive performance comparable to that of the GP model while enjoying lower computational cost over it. The predictive performance of GP-DDF and GP-\(\hbox {DDF}^+\) can be improved by increasing the size of \(\mathcal {S}\) at the expense of greater time and communication overhead.
 
4
Naively, an agent can delay transfer learning by simply storing a separate local summary based on the support set for every previously visited local area, which is not memory-efficient.
 
5
Multiple backups of the local summary and support set for the same local area may exist if agents leave this area at the same time, which rarely happens. In this case, agent i should retrieve (and remove) all these backups from the agents storing them.
 
6
Alternatively, active learning can be used to select an informative support set a priori for each local area (Chen et al. 2015). Empirically, this yields little performance improvement due to a sufficiently dense (yet small) support set uniformly distributed over the local area and slightly beyond its boundary by \(10\%\) of its width.
 
7
Local GPs result from a sparse block-diagonal \(\varSigma _{\mathcal {D}\mathcal {D}}\) (2).
 
8
The predictive performance of centralized PITC corresponds to that of GP-DDF, as discussed in Sect. 2.2. Hence, the RMSE of centralized PITC coincides exactly with that of GP-DDF in Fig. 8.
 
9
The incurred time of centralized PITC is slightly less than that of GP-DDF (Fig. 8) increased by a factor of the total number of agents. This agrees with the analysis of the time complexity of PITC versus GP-DDF in Sect. 2.2. This can also be observed in Fig. 9 where the incurred time of GP-DDF increases by nearly two fold when the number of agents is halved.
 
10
If the subset sizes differ, then “virtual” locations are added to each subset to make all subsets to be of the same size as \(T\triangleq \arg \max _{s\in \mathcal {S}} |\mathcal {D}_{is}|\) (\(T'\triangleq \arg \max _{s\in \mathcal {S}} |\mathcal {S}'_{s}|\)). The virtual locations added to \(\mathcal {D}_{is}\) (\(\mathcal {S}'_{s}\)) are chosen as \(s\in \mathcal {S}\) so that they do not induce additional errors but will loosen the bound.
 
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Metadaten
Titel
Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception
verfasst von
Ruofei Ouyang
Bryan Kian Hsiang Low
Publikationsdatum
28.01.2019
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3-4/2020
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-018-09826-z

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