Consistent Multi-robot Object Matching via QuickMatch

Serlin, Zachary; Sookraj, Brandon; Belta, Călin; Tron, Roberto

doi:10.1007/978-3-030-33950-0_64

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…This article is an extension of previous work presented at the International Symposium of Experimental Robotics Serlin (2018) and the International Conference on Computer Vision Tron (2017). The primary extensions from this prior work are:…”

Section: Related Workmentioning

confidence: 78%

Distributed and Consistent Multi-Image Feature Matching via QuickMatch

Serlin¹,

Yang²,

Sookraj³

et al. 2019

Preprint

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In this work we consider the multi-image object matching problem, extend a centralized solution of the problem to a distributed solution, and present an experimental application of the centralized solution. Multi-image feature matching is a keystone of many applications, including simultaneous localization and mapping, homography, object detection, and structure from motion. We first review the QuickMatch algorithm for multi-image feature matching. We then present a scheme for distributing sets of features across computational units (agents) that largely preserves feature match quality and minimizes communication between agents (avoiding, in particular, the need of flooding all data to all agents). Finally, we show how QuickMatch performs on an object matching test with low quality images. The centralized QuickMatch algorithm is compared to other standard matching algorithms, while the Distributed QuickMatch algorithm is compared to the centralized algorithm in terms of preservation of match consistency. The presented experiment shows that QuickMatch matches features across a large number of images and features in larger numbers and more accurately than standard techniques.

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Section: Related Workmentioning

confidence: 78%

Distributed and Consistent Multi-Image Feature Matching via QuickMatch

Serlin¹,

Yang²,

Sookraj³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This article is an extension of previous work presented at the International Symposium of Experimental Robotics (Serlin et al, 2018) and the International Conference on Computer Vision (Tron et al, 2017). The primary extensions from this prior work are as follows.…”

Section: Introductionmentioning

confidence: 86%

Distributed and consistent multi-image feature matching via QuickMatch

Serlin

Yang

Sookraj

et al. 2020

The International Journal of Robotics Research

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In this work, we consider the multi-image object matching problem in distributed networks of robots. Multi-image feature matching is a keystone of many applications, including Simultaneous Localization and Mapping, homography, object detection, and Structure from Motion. We first review the QuickMatch algorithm for multi-image feature matching. We then present NetMatch, an algorithm for distributing sets of features across computational units (agents) that largely preserves feature match quality and minimizes communication between agents (avoiding, in particular, the need to flood all data to all agents). Finally, we present an experimental application of both QuickMatch and NetMatch on an object matching test with low-quality images. The QuickMatch and NetMatch algorithms are compared with other standard matching algorithms in terms of preservation of match consistency. Our experiments show that QuickMatch and Netmatch can scale to larger numbers of images and features, and match more accurately than standard techniques.

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“…This also implies that the robot cannot establish whether a request entering and leaving the sensing area multiple times is the same request. Tracking is outside the scope of this article, but has been considered in Serlin et al (2018a,b) with LTL constraints, and in Pierson et al (2016) as active pursuit with exclusion zones. The set of regions corresponding to local obstacles present in the environment at time t ≥ 0 is denoted by R L ( t ) .…”

Section: Problem Formulationmentioning

confidence: 99%

Reactive sampling-based path planning with temporal logic specifications

Vasile

Belta

2020

The International Journal of Robotics Research

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We develop a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The mission specification has two parts: (1) a global specification given as a linear temporal logic (LTL) formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula; and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The off-line algorithm has four main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which implies low complexity for the overall method. Third, it is probabilistically complete. Fourth, under some mild assumptions, it has the best possible complexity bound. The on-line algorithm leverages ideas from LTL monitoring and potential functions to ensure progress towards the satisfaction of the global specification while servicing locally sensed requests. Examples and experimental trials illustrating the usefulness and the performance of the framework are included.

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Consistent Multi-robot Object Matching via QuickMatch

Cited by 3 publications

References 15 publications

Distributed and Consistent Multi-Image Feature Matching via QuickMatch

Distributed and Consistent Multi-Image Feature Matching via QuickMatch

Distributed and consistent multi-image feature matching via QuickMatch

Reactive sampling-based path planning with temporal logic specifications

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