A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

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A novel approach to model the land mobile satellite channel through reversible jump markov chain monte carlo technique

IEEE Transactions on Wireless Communications ◽

10.1109/twc.2008.05544 ◽

2008 ◽

Vol 7 (2) ◽

pp. 532-542 ◽

Cited By ~ 9

Author(s):

Clemence Alasseur ◽

Sandro Scalise ◽

Lionel Husson ◽

Harald Ernst

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Monte Carlo Technique ◽

Reversible Jump ◽

Novel Approach ◽

Mobile Satellite

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Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

Autonomous Robots ◽

10.1007/s10514-021-10013-w ◽

2021 ◽

Vol 45 (6) ◽

pp. 843-857

Author(s):

Russell Buchanan ◽

Jakub Bednarek ◽

Marco Camurri ◽

Michał R. Nowicki ◽

Krzysztof Walas ◽

...

Keyword(s):

Monte Carlo ◽

Estimation Error ◽

Extreme Environments ◽

Legged Robot ◽

Terrain Classification ◽

Localization Algorithm ◽

Geometric Information ◽

Contact Points ◽

Monte Carlo Localization ◽

Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

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Semantic Monte-Carlo localization in changing environments using RGB-D cameras

2017 European Conference on Mobile Robots (ECMR) ◽

10.1109/ecmr.2017.8098711 ◽

2017 ◽

Cited By ~ 2

Author(s):

Marian Himstedt ◽

Erik Maehle

Keyword(s):

Monte Carlo ◽

Changing Environments ◽

Monte Carlo Localization

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A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4627-2018 ◽

2018 ◽

Vol 11 (8) ◽

pp. 4627-4643 ◽

Cited By ~ 7

Author(s):

Simon Pfreundschuh ◽

Patrick Eriksson ◽

David Duncan ◽

Bengt Rydberg ◽

Nina Håkansson ◽

...

Keyword(s):

Neural Network ◽

Monte Carlo ◽

Monte Carlo Integration ◽

A Posteriori ◽

Retrieval Performance ◽

Neural Network Approach ◽

Novel Approach ◽

Uncertainty Estimates ◽

Non Gaussian ◽

Similar Accuracy

Abstract. A neural-network-based method, quantile regression neural networks (QRNNs), is proposed as a novel approach to estimating the a posteriori distribution of Bayesian remote sensing retrievals. The advantage of QRNNs over conventional neural network retrievals is that they learn to predict not only a single retrieval value but also the associated, case-specific uncertainties. In this study, the retrieval performance of QRNNs is characterized and compared to that of other state-of-the-art retrieval methods. A synthetic retrieval scenario is presented and used as a validation case for the application of QRNNs to Bayesian retrieval problems. The QRNN retrieval performance is evaluated against Markov chain Monte Carlo simulation and another Bayesian method based on Monte Carlo integration over a retrieval database. The scenario is also used to investigate how different hyperparameter configurations and training set sizes affect the retrieval performance. In the second part of the study, QRNNs are applied to the retrieval of cloud top pressure from observations by the Moderate Resolution Imaging Spectroradiometer (MODIS). It is shown that QRNNs are not only capable of achieving similar accuracy to standard neural network retrievals but also provide statistically consistent uncertainty estimates for non-Gaussian retrieval errors. The results presented in this work show that QRNNs are able to combine the flexibility and computational efficiency of the machine learning approach with the theoretically sound handling of uncertainties of the Bayesian framework. Together with this article, a Python implementation of QRNNs is released through a public repository to make the method available to the scientific community.

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