Optimal placement of passive sensors for robot localisation

Zenatti, Fabiano; Fontanelli, Daniele; Palopoli, Luigi; Macii, David; Nazemzadeh, Payam

doi:10.1109/iros.2016.7759675

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…Empirical placement and several tests is one way of finding optimal sensor placement, as performed for categorizing type of physical activity and corresponding energy expenditure in older adults using wearable accelerometers [7]. The implementation of Markov chains and solving the boolean optimization problem derived from it, is used as method for optimally placing markers and minimizing their number in large public environments for robot localization [8]. These robots would assist older adults in moving around in such spaces.…”

Section: Related Workmentioning

confidence: 99%

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

2023

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This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident's tracked location. The resulting clusters identify the possible position of a sensor and its field of view. By using a sequence of clusters as input to a Recurrent Neural Network, we evaluate our method on the task of activity recognition and prediction. These results are compared to using sensor events as input sequence, from motion sensors that were installed empirically in the same homes. Different clustering methods are investigated and all outperform the installed motion sensors, achieving a significant increase of prediction accuracy and F1-score.

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Section: Related Workmentioning

confidence: 99%

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

2023

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“…Observe that ( 7) returns an estimate of the robot planar position (x k , y k ) provided that the Moore-Penrose pseudoinverse of Σ is invertible (which is always true if the anchor nodes are not collinear). Finally, the trajectory angle θ k can be estimated indirectly from (6) once the pair of values (x k , y k ) and (x k+1 , y k+1 ) in two consecutive positions are estimated through (7). In particular, if R(φ k ) denotes the twodimensional rotation matrix of angle φ k , we have that…”

Section: B Observability Analysismentioning

confidence: 99%

“…Therefore, a number of alternative technological solutions have to be used for robot position tracking. They include (but are not limited to) power fingerprinting of Wi-Fi signals [2], detection of radio frequency identification (RFID) tags [3], [4], computer vision techniques using both natural [5] and artificial landmarks [6]- [8], and Light Detection and Ranging (LiDAR) sensors [9]. Particularly relevant to this paper are the multilateration solutions that exploit the distance measured from a set of wireless nodes (briefly referred to as anchors in the following [10]), with a known position in a given reference frame.…”

Section: Introductionmentioning

confidence: 99%

An Uncertainty-Driven and Observability-Based State Estimator for Nonholonomic Robots

Fontanelli

Shamsfakhr

Macii

et al. 2021

IEEE Trans. Instrum. Meas.

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The problem addressed in this paper is the localisation of a mobile robot using a combination of on-board sensors and Ultra-Wideband (UWB) beacons. By using a discrete-time formulation of the system's kinematics, we identify the geometric conditions that make the system globally observable and cast the state estimation problem into the framework of least-square optimisation. The observability filter thus obtained is remarkably different from classic Bayesian filters, such as the Kalman Filter, since it does not need a-priori stochastic models of process and measurement uncertainty contributions and thus proves to have better performance than the Kalman filters if such contributions are partly unknown or differ from the expected values. A second important outcome of this work is the analytical study of the uncertainty propagation. The effectiveness of the designed filter, the validity of the theoretical analysis of estimation uncertainties and the comparisons with a state-of-the-art extended Kalman filter (EKF) are corroborated by extensive simulations and are validated experimentally.

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“…A basic technique is to conduct dead reckoning using interoceptive sensors such as wheel encoders and an IMU to incrementally track the robot motion using Bayesian filtering. However, dead reckoning is subject to drift, hence the filter must be periodically reset using extra information such as celestial positioning [15], [16], fiducial markers [17], [18], or image matching [19], [20].…”

Section: Localisationmentioning

confidence: 99%

Autonomy and Perception for Space Mining

Sachdeva¹,

Hammond²,

Bockman³

et al. 2021

Preprint

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Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. Specifically, we describe and report the results of robotic vision algorithms that we developed for Phase 2 of the NASA Space Robotics Challenge, which was framed in the context of autonomous collaborative robots for mining on the Moon. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. We show how machine learning-enabled vision could help alleviate the challenges posed by the lunar environment. A robust multirobot coordinator was also developed to achieve long-term operation and effective collaboration between robots 1 .

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Optimal placement of passive sensors for robot localisation

Cited by 14 publications

References 24 publications

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

An Uncertainty-Driven and Observability-Based State Estimator for Nonholonomic Robots

Autonomy and Perception for Space Mining

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