The Range Beacon Placement Problem for Robot Navigation

Allen, River; Macmillan, Neil A.; Marinakis, Dimitri; Nishat, Rahnuma Islam; Rahman, Rayhan; Whitesides, Sue

doi:10.1109/crv.2014.28

Cited by 7 publications

(5 citation statements)

References 25 publications

(22 reference statements)

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“…Krause et al formulate an optimal sensor problem by placing sensors to maximize the mutual information of sensed and unsensed locations using learned Gaussian processes [29]. Beinhofer et al [30] and Allen et al [31] formulate sensor placement problems to place artificial navigation landmarks and sonar beacons, respectively, along pre-defined trajectories for localization. Vitus and Tomlin [32] formulated an optimal sensor placement problem in which a vehicle deploys a limited set of sensor beacons in the environment to minimize its navigation estimation error.…”

Section: Landmark Selectionmentioning

confidence: 99%

Graph-based terrain relative navigation with optimal landmark database selection

Steiner

Brady

Hoffman

2015

2015 IEEE Aerospace Conference

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Terrain relative navigation (TRN) offers a means to constrain absolute vehicle position and attitude without using a GPS receiver or star camera, such as for unmanned aerial vehicles or planetary landing spacecraft, using a pre-computed terrain database of distinctive landmarks in the operational environment. However, depending on the length of the planned trajectory, these terrain landmark databases may grow prohibitively large for the onboard data storage, processing, and communication capabilities of these types of vehicles.In this paper, we introduce a definition of landmark utilitywhich encapsulates the observation probability of a landmark and its spatial distribution relative to nearby landmarks in the database -as a measure of the value of potential line-ofsight measurements to that landmark. We additionally present an efficient algorithm to sort all potential landmarks in an environment based on their relative utility without access to the actual sensor measurements or trajectory. This enables pre-determination of a limited-size terrain landmark database containing the N -best landmarks in the environment, which is applicable to any landmark-based TRN system.To evaluate our landmark selection algorithm, we additionally introduce an incremental smoothing-based approach to TRN using a Bayesian factor graph representation. This system is capable of overcoming several challenges associated with TRN systems, including relinearization of past measurements, simultaneously utilizing pre-mapped and opportunistic features in a common estimator, and efficient sensor fusion in a common, probabilistic framework. We provide results showing the benefits of our utility-based landmark selection compared to alternative selection approaches, using evaluation metrics independent of any particular TRN estimation framework and the navigation performance of our graph-based TRN system.

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Section: Landmark Selectionmentioning

confidence: 99%

Graph-based terrain relative navigation with optimal landmark database selection

Steiner

Brady

Hoffman

2015

2015 IEEE Aerospace Conference

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“…Unlike outdoor spaces, signal emitting and processing devices in indoor spaces should consider the permeability and diffraction issues as there are many walls, columns and obstacles that obstruct the line of sight from the device [34]. There are also other issues regarding various device characteristics such as beacons with limited effective ranges and angular restrictions [35], and with different power levels [36].…”

Section: Device Placement Problemsmentioning

confidence: 99%

A Flexible Framework for Covering and Partitioning Problems in Indoor Spaces

Kim

Cho

2020

IJGI

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Utilizing indoor spaces has become important with the progress of localization and positioning technologies. Covering and partitioning problems play an important role in managing, indexing, and analyzing spatial data. In this paper, we propose a multi-stage framework for indoor space partitioning, each stage of which can be flexibly adjusted according to target applications. One of the main features of our framework is the parameterized constraint, which characterizes the properties and restrictions of unit geometries used for the covering and partitioning tasks formulated as the binary linear programs. It enables us to apply the proposed method to various problems by simply changing the constraint parameter. We present basic constraints that are widely used in many covering and partitioning problems regarding the indoor space applications along with several techniques that simplify the computation process. We apply it to particular applications, device placement and route planning problems, in order to give examples of the use of our framework in the perspective on how to design a constraint and how to use the resulting partitions. We also demonstrate the effectiveness with experimental results compared to baseline methods.

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“…Most of the time, the last two objectives are based on the static trilateration method. Allen et al [3] proposed a localization utility function, based on the number of range measurements available at a given position. The more range measurements, the better the localization is, and ideally we seek to have at least three range measurements at each positions to be able to perform trilateration.…”

Section: Related Workmentioning

confidence: 99%

“…Three fitness functions are considered, all based on existing works but with a slighlty different formulation. The first one (termed nbRanges) is related to the coverage [3], and computes the mean number of range measurements available at each position of the workspace. The second (termed detFimThresh), is related to the localization accuracy, and uses the FIM determinant (|F IM |) as in [11].…”

Section: Fitness Functionsmentioning

confidence: 99%

Limits of trilateration-based sensor placement algorithms

Génevé

Kermorgant

Laroche

2017

2017 IEEE Sensors Applications Symposium (SAS)

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Abstract-To localize a mobile vehicle in a predefined area, it is possible to resort to a positioning system using beacons. The questions arising then are: what is the right number of beacons to deploy, and where should they be positioned to accurately localize the target. With a sensor placement algorithm, it is possible to generate a configuration of the beacons that optimize some quality criteria. However, designing the objectives to optimize is not simple, and the solution of the sensor placement algorithm may not always ensures a good localization in practice. This work evaluates the impact of the objectives of a sensor placement algorithm on different localization techniques. We show that criteria based on trilateration are not sufficient anymore when the localization is done with other techniques than trilateration. Indeed, data fusion-based localization algorithms use additional information such as odometry, and are less sensitive to poor coverage or poor beacon configurations than trilateration. Thus, designing new criteria taking into account the dynamics of the vehicle would probably improve further the placements and use less beacons for the same performance.

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The Range Beacon Placement Problem for Robot Navigation

Cited by 7 publications

References 25 publications

Graph-based terrain relative navigation with optimal landmark database selection

Graph-based terrain relative navigation with optimal landmark database selection

A Flexible Framework for Covering and Partitioning Problems in Indoor Spaces

Limits of trilateration-based sensor placement algorithms

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