Robust sensor cloud localization from range measurements

Dubbelman, Gijs; Duisterwinke, Erik; Demi, Libertario; Talnishnikh, Elena; Wörtche, H. J.; Bergmans, J.W.M.

doi:10.1109/iros.2014.6943099

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…Given an initial graph  , new nodes can be added as illustrated in Figure 2 and detailed in [6]. In an ideal case (without noise), every candidate node, c, of which the distances to four non-coplanar nodes with known positions …”

Section: Initial Seed Selectionmentioning

confidence: 99%

“…In order to reduce this error build-up, a global non-linear optimizer algorithm is executed as described in [6] to minimize the cost function Equation (1). It can easily solve graph problems consisting of thousands of nodes and edges.…”

Section: Robust Non-linear Refinementmentioning

confidence: 99%

“…All experiments are performed with a total number of 400 N = nodes. For similar work with a smaller and larger number of nodes, the reader is referred to our earlier work [6] and [7].…”

Section: Parameter Sweeps and Breaking Pointsmentioning

confidence: 99%

“…the edges  in graph  ), the challenge is to obtain a robust initial estimate of the node positions in order for non-linear graph optimization algorithms to succeed. In order to robustly obtain such an initial estimate, our offline 1 reconstruction algorithm uses a Random Sampling Consensus (RANSAC) method applied to general lateration techniques [6] [7]. To the best of our knowledge, the novel reconstruction algorithm presented in this work, is one of the most robust algorithms that is able to perform 3-D reconstruction of large sensor swarms.…”

mentioning

confidence: 99%

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Robust Reconstruction of Sensor Swarms Floating through Enclosed Environments

Duisterwinkel

Dubbelman²,

Demi³

et al. 2018

WSN

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A novel type of application for the exploration of enclosed or otherwise difficult to access environments requires large quantities of miniaturized sensor nodes to perform measurements while they traverse the environment in a "go with the flow" approach. Examples of these are the exploration of underground cavities and the inspection of industrial pipelines or mixing tanks, all of which have in common that the environments are difficult to access and do not allow position determination using e.g. GPS or similar techniques. The sensor nodes need to be scaled down towards the millimetre range in order to physically fit through the narrowest of parts in the environments and should measure distances between each other in order to enable the reconstruction of their positions relative to each other in offline analysis. Reaching those levels of miniaturization and enabling reconstruction functionality requires: 1) novel reconstruction algorithms that can deal with the specific measurement limitations and imperfections of millimetre-sized nodes, and 2) improved understanding of the relation between the highly constraint hardware design space of the sensor nodes and the reconstruction algorithms. To this end, this work provides a novel and highly robust sensor swarm reconstruction algorithm and studies the effect of hardware design trade-offs on its performance. Our findings based on extensive simulations, which push the reconstruction algorithm to its breaking point, provide important guidelines for the future development of millimetre-sized sensor nodes.

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Section: Initial Seed Selectionmentioning

confidence: 99%

Section: Robust Non-linear Refinementmentioning

confidence: 99%

“…All experiments are performed with a total number of 400 N = nodes. For similar work with a smaller and larger number of nodes, the reader is referred to our earlier work [6] and [7].…”

Section: Parameter Sweeps and Breaking Pointsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Robust Reconstruction of Sensor Swarms Floating through Enclosed Environments

Duisterwinkel

Dubbelman²,

Demi³

et al. 2018

WSN

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method inherits the simplicity and efficiency of trilateration, and improves the ambiguity by identifying more localizable nodes. Reference [ 26 ] proposed the random sample consensus(RANSAC)-based trilateration method to robustly estimate an initial pose graph, which models the locations of sensor platforms. However, it lacks the consideration of two intersection points and non-existence of the intersection points, thus it cannot solve the uncertainty problem.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems

Yue

Chen

et al. 2017

Sensors

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Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the range of the circle within a certain confidence interval is proposed. In the method, the confidence interval is estimated based on the Cramér–Rao lower bound of the time of flight (TOF) measurement. Furthermore, an intersection determination method is proposed to select the intersection point with higher confidence level. The simulation and experimental results show the superiority of the proposed method in localization accuracy and robustness to noise compared to the conventional trilateration method, e.g., the centroid-based and least squares based trilateration methods.

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