Simultaneous mobile robot and radio node localization in wireless networks

Graefenstein, Juergen; Albert, Amos; Biber, Peter; Schilling, Andreas

doi:10.1109/ipin.2010.5647573

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improvements may be achieved by augmenting the observations, for example with the signal strength and a corresponding signal strength decay model. Another possibility would be to use a particle filter to obtain good initial EKF estimates [5]. Nevertheless, as we will see later, the good directional estimates are sufficient to navigate the robot to the transponder in our experiments.…”

Section: A Identification: Localization On Simple Mapsmentioning

confidence: 96%

See 1 more Smart Citation

Path Planning Under Uncertainty to Localize mmWave Sources

Pfeiffer¹,

Jia²,

Mingsheng³

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (∼ 300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

show abstract

Section: A Identification: Localization On Simple Mapsmentioning

confidence: 96%

“…We chose a redundant representation to avoid discontinuities for example associated with the arctan [5]. Note however that we measure directly α i .…”

Section: A Line-of-sight Casementioning

confidence: 99%

Path Planning Under Uncertainty to Localize mmWave Sources

Pfeiffer¹,

Jia²,

Mingsheng³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Lee et al, 2011;Lim, 2009) or a mobile robot (e.g. Caballero et al, 2010;Djugash et al, 2006;Graefenstein et al, 2010;Kurth et al, 2003), and mapping of static wireless devices based on the RF signal measurements is developed. The static RF nodes can be access points in a wifi network (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, AOA-based approaches are used, which are called bearing-only SLAM (e.g. Graefenstein et al, 2010). It should be mentioned that in the available studies, AOA observations are used less in comparison with the RSSI observations.…”

Section: Related Workmentioning

confidence: 99%

SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

Dehghan

Moradi

2015

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV's localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

show abstract