Robust cooperative localization in a dynamic environment using factor graphs and probability data association filter

Gulati, Dhiraj; Zhang, Feihu; Malovetz, Daniel; Clarke, Daniel; Knoll, Alois

doi:10.23919/icif.2017.8009654

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…• Solve the problem of clutter using only the optimizer and remain independent of any other methods. • Previous work [21] demonstrated the results using 2 vehicles simulation. The PDA Filter uses a Kalman Filter and tracks the Topology Measurements, this implies we require a Model for the Topology Measurements.…”

Section: Introductionmentioning

confidence: 87%

“…For sake of completeness, we describe a simple cooperative localization scenario in presence of clutter. The basic problem definition remains same as in [21] and can be seen in the Fig. 1.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Graph-based methods mostly rely on the front end or any other association algorithm to handle the outliers. Our previous solution [21] used PDA Filter along side the Topology Factor solution to tackle the challenge of clutter measurements. The solution tracked the resulting Topology Measurements and applied PDA Filter to reject any measurements which did not meet the threshold and the remaining were assigned a probability.…”

Section: Introductionmentioning

confidence: 99%

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Robust Vehicle Infrastructure Cooperative Localization in Presence of Clutter

Gulati

Aravantinos

Somani

et al. 2018

2018 21st International Conference on Information Fusion (FUSION)

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One of the primary challenges for a successful Highly Assisted and/or Autonomous Vehicle is its localization. To improve the precision of location of the vehicle, not only the internal sensors are being used, but also using data from external sensors is attracting increasing attention from the research community. One such proposed sensor is an infrastructure RADAR which can be used to improve the localization of the egovehicle. Although a RADAR indeed is a supplementary source of information, it suffers a unique type of clutter which have trajectories like real objects and can therefore result in "ghost measurements", i.e., measurements which do not correspond to any real vehicles. This deteriorates the quality of the fused state estimates. This paper proposes a robust method to fuse the RADAR readings in presence of such outliers. This methodology builds upon a previously proposed solution where the problem was formulated as a factor graph. The RADAR measurements were added as a novel constraint of sum of inter-vehicle distance, called Topology Factor. Our previous work assumed clutter free environment. This paper proposes a novel robust Topology Factor which is also resilient against above mentioned outliers. Simulations (based on real data) show promising results in the direction of lowering the degradation of fused state estimates in presence of such clutter.

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Section: Introductionmentioning

confidence: 87%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Vehicle Infrastructure Cooperative Localization in Presence of Clutter

Gulati

Aravantinos

Somani

et al. 2018

2018 21st International Conference on Information Fusion (FUSION)

Self Cite

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“…Suppose the proposed integrated navigation algorithm is conducted from the point [37.4 • E, 49.86 • N]. INS's accumulated error is simulated by adding different rigid transformations to the real trajectory [21]. The measurement error is Gaussian white noise.…”

Section: Effectiveness Of the Proposed Algorithmmentioning

confidence: 99%

An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

Duan

Xiao

et al. 2019

Electronics

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To improve the positioning accuracy of an inertial/geomagnetic integrated navigation algorithm, a combined navigation method based on matching strategy and hierarchical filtering is proposed. First, the PDA-ICCP geomagnetic matching algorithm is improved. On basis of evaluating the distribution of magnetic measurements, a number of controllable magnetic values are regenerated to participate in the geomagnetic matching algorithm (GMA). As a result, accuracy of the matching algorithm is ensured and its efficiency is improved. Secondly, the integrated navigation filter is designed based on the hierarchical filtering strategy, in which the navigation information of the geomagnetic matching module and inertial navigation module are respectively filtered and fused in the main filter. In this way, the shortcoming that GMA is unable to provide continuous and real-time navigation information is overcome. Meanwhile, precision of the inertial/geomagnetic integrated navigation algorithm is improved. Finally, the feasibility and validity of the proposed algorithm are verified by simulation and physical experiments. Compared with the integrated filtering algorithm which directly uses the error equation of inertial navigation system (INS) as the state equation, the proposed hierarchical filtering algorithm can achieve higher positioning precision.

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“…It assumes that there is only one target in the clutter environment. If the trajectory of the target is formed and multiple echoes are detected, then all the effective echoes may come from the target, and each of them has a different confidence probability (Gulati et al, 2017).…”

Section: Basic Principle Of M-iccp Algorithmmentioning

confidence: 99%

An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

Xiao

Duan

et al. 2019

J. Navigation

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The Iterated Closest Contour Point (ICCP) algorithm is widely used in geomagnetic navigation. In order to enhance the anti-interference performance of the ICCP, an improved algorithm is proposed. First, the principle of delta modulation is introduced to generate a geomagnetic matching sequence according to the magnetic fluctuations, this assists finding the optimal quantitative step and matching length; thus, the algorithm's accuracy and real-time performance are improved. Second, in order to solve the problem of geomagnetic matching under an interference environment, a Probability Data Association (PDA) algorithm based on regenerated measurements is adopted. The ideal magnetic value is regarded as a target, and the measured values within the confidence region are taken as the effective measurements of the target. Each of them will give an estimation of the vehicle's position. Considering the constraints of a vehicle's kinematic performance, its final position can be obtained by fusing all effective estimations with the PDA algorithm. Simulation and semi-physical experiments have verified the feasibility and effectiveness of the proposed algorithm. The Regenerated Measurements (RM)-PDA algorithm shows better performance and can be used in practical applications.

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Robust cooperative localization in a dynamic environment using factor graphs and probability data association filter

Cited by 7 publications

References 17 publications

Robust Vehicle Infrastructure Cooperative Localization in Presence of Clutter

Robust Vehicle Infrastructure Cooperative Localization in Presence of Clutter

An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

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