Author Archives: Juan-antonio Fernández-madrigal

Improving orientation estimation in a mobile robot for doing better odometry

August 26, 2017 08:05 ,

M.T. Sabet, H.R. Mohammadi Daniali, A.R. Fathi, E. Alizadeh, Experimental analysis of a low-cost dead reckoning navigation system for a land vehicle using a robust AHRS, Robotics and Autonomous Systems, Volume 95, 2017, Pages 37-51, DOI: 10.1016/j.robot.2017.05.010.

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue especially when the localization sensors such as GPS are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework an Extended Kalman Filter (EKF) is utilized for estimation of attitude, heading and gyroscope sensor bias using a low-cost MEMS inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study using the proposed attitude and heading reference system (AHRS) and an odometer sensor, a low-cost aided DR navigation system has been designed. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for a land vehicle. The results indicate that the roll, pitch and heading are estimated by mean value errors about 0.83%, 0.68% and 1.13%, respectively. Moreover, they indicate that a relative navigation error about 3% of the traveling distance can be achieved using the developed approach in during GPS outages.

Posted in: Mobile Robot Localization, Robot sensors , Tagged: ,

On how the simplification on physics made in computer games for real-time execution can explain the simplification on physics made by infants when understanding the world

August 17, 2017 11:39 ,

Tomer D. Ullman, Elizabeth Spelke, Peter Battaglia, Joshua B. Tenenbaum, Mind Games: Game Engines as an Architecture for Intuitive Physics, Trends in Cognitive Sciences, Volume 21, Issue 9, 2017, Pages 649-665, DOI: 10.1016/j.tics.2017.05.012.

We explore the hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games. We describe the key features of game physics engines and their parallels in human mental representation, focusing especially on the intuitive physics of young infants where the hypothesis helps to unify many classic and otherwise puzzling phenomena, and may provide the basis for a computational account of how the physical knowledge of infants develops. This hypothesis also explains several ‘physics illusions’, and helps to inform the development of artificial intelligence (AI) systems with more human-like common sense.

Posted in: Probability and statistics, Psycho-physiological bases of engineering , Tagged:

Optimization algorithms inspired in chemical reactions

August 15, 2017 10:18 ,

Nazmul Siddique, Hojjat Adeli, Nature-Inspired Chemical Reaction Optimisation Algorithms, Cognitive Computation, Volume 9, Issue 4, pp 411–422, DOI: 10.1007/s12559-017-9485-1.

Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised.

Posted in: Uncategorized , Tagged: ,

Identification of beacons for localization by using LEDs with light patterns as IDs

August 10, 2017 10:10 ,

G. Simon, G. Zachár and G. Vakulya, Lookup: Robust and Accurate Indoor Localization Using Visible Light Communication, IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 9, pp. 2337-2348, DOI: 10.1109/TIM.2017.2707878.

A novel indoor localization system is presented, where LED beacons are utilized to determine the position of the target sensor, including a camera, an inclinometer, and a magnetometer. The beacons, which can be a part of the existing lighting infrastructure, transmit their identifiers for long distances using visible light communication techniques. The sensor is able to sense and detect the high-frequency (flicker free) code by properly undersampling the transmitted signal. The localization is performed using novel geometric and consensus-based techniques, which tolerate well measurement inaccuracies and sporadic outliers. The performance of the system is analyzed using simulations and real measurements. According to large-scale tests in realistic environments, the accuracy of the proposed system is in the low decimeter range.

Posted in: Mobile Robot Localization , Tagged: ,

The problem of the interdependence among particles in PF after the resampling step, and an approach to solve it

August 10, 2017 10:05 ,

R. Lamberti, Y. Petetin, F. Desbouvries and F. Septier, Independent Resampling Sequential Monte Carlo Algorithms, IEEE Transactions on Signal Processing, vol. 65, no. 20, pp. 5318-5333, DOI: 10.1109/TSP.2017.2726971.

Sequential Monte Carlo algorithms, or particle filters, are Bayesian filtering algorithms, which propagate in time a discrete and random approximation of the a posteriori distribution of interest. Such algorithms are based on importance sampling with a bootstrap resampling step, which aims at struggling against weight degeneracy. However, in some situations (informative measurements, high-dimensional model), the resampling step can prove inefficient. In this paper, we revisit the fundamental resampling mechanism, which leads us back to Rubin’s static resampling mechanism. We propose an alternative rejuvenation scheme in which the resampled particles share the same marginal distribution as in the classical setup, but are now independent. This set of independent particles provides a new alternative to compute a moment of the target distribution and the resulting estimate is analyzed through a CLT. We next adapt our results to the dynamic case and propose a particle filtering algorithm based on independent resampling. This algorithm can be seen as a particular auxiliary particle filter algorithm with a relevant choice of the first-stage weights and instrumental distributions. Finally, we validate our results via simulations, which carefully take into account the computational budget.

Posted in: Bayesian filtering , Tagged: ,

Using bad results during policy iteration, and not only good ones, to improve the learning process

August 10, 2017 10:00 ,

A. Colomé and C. Torras, Dual REPS: A Generalization of Relative Entropy Policy Search Exploiting Bad Experiences, IEEE Transactions on Robotics, vol. 33, no. 4, pp. 978-985, DOI: 10.1109/TRO.2017.2679202.

Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only. This means that experiments yielding a poor performance are usually discarded or given too little influence on the policy update. In this paper, we propose a generalization of the relative entropy policy search (REPS) algorithm that takes bad experiences into consideration when computing a policy. The proposed approach, named dual REPS (DREPS) following the philosophical interpretation of the duality between good and bad, finds clusters of experimental data yielding a poor behavior and adds them to the optimization problem as a repulsive constraint. Thus, considering that there is a duality between good and bad data samples, both are taken into account in the stochastic search for a policy. Additionally, a cluster with the best samples may be included as an attractor to enforce faster convergence to a single optimal solution in multimodal problems. We first tested our proposed approach in a simulated reinforcement learning setting and found that DREPS considerably speeds up the learning process, especially during the early optimization steps and in cases where other approaches get trapped in between several alternative maxima. Further experiments in which a real robot had to learn a task with a multimodal reward function confirm the advantages of our proposed approach with respect to REPS.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Taking into account explicitly the dynamics of the environment, and in particular the diverse frequencies of changes, for mobile robot mapping

August 10, 2017 09:56 ,

T. Krajník, J. P. Fentanes, J. M. Santos and T. Duckett, FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments, IEEE Transactions on Robotics, vol. 33, no. 4, pp. 964-977, DOI: 10.1109/TRO.2017.2665664.

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot’s long-term performance in changing environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model’s predictive capabilities improve mobile robot localization and navigation in changing environments.

Posted in: Mobile robot mapping , Tagged: ,

Interleaving segmentation (semantics) and dense 3D reconstruction (metrics)

August 10, 2017 09:47 ,

C. Häne, C. Zach, A. Cohen and M. Pollefeys, Dense Semantic 3D Reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 9, pp. 1730-1743, DOI: 10.1109/TPAMI.2016.2613051.

Both image segmentation and dense 3D modeling from images represent an intrinsically ill-posed problem. Strong regularizers are therefore required to constrain the solutions from being `too noisy’. These priors generally yield overly smooth reconstructions and/or segmentations in certain regions while they fail to constrain the solution sufficiently in other areas. In this paper, we argue that image segmentation and dense 3D reconstruction contribute valuable information to each other’s task. As a consequence, we propose a mathematical framework to formulate and solve a joint segmentation and dense reconstruction problem. On the one hand knowing about the semantic class of the geometry provides information about the likelihood of the surface direction. On the other hand the surface direction provides information about the likelihood of the semantic class. Experimental results on several data sets highlight the advantages of our joint formulation. We show how weakly observed surfaces are reconstructed more faithfully compared to a geometry only reconstruction. Thanks to the volumetric nature of our formulation we also infer surfaces which cannot be directly observed for example the surface between the ground and a building. Finally, our method returns a semantic segmentation which is consistent across the whole dataset.

Posted in: Computer vision , Tagged: ,

Clustering in hypergraphs

August 10, 2017 09:45 ,

P. Purkait, T. J. Chin, A. Sadri and D. Suter, Clustering with Hypergraphs: The Case for Large Hyperedges, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 9, pp. 1697-1711, DOI: 10.1109/TPAMI.2016.2614980.

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.

Posted in: Graph theory , Tagged: ,

An interesting soft-partition method based on hierarchical graphs (trees, actually) applied to topic detection in documents

August 9, 2017 09:37 ,

Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K.M. Poon, Zhourong Chen, Farhan Khawar, Latent tree models for hierarchical topic detection, Artificial Intelligence, Volume 250, 2017, Pages 105-124, DOI: 10.1016/j.artint.2017.06.004.

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables that represent word co-occurrence patterns or co-occurrences of such patterns. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. In comparison with LDA-based methods, a key advantage of the new method is that it represents co-occurrence patterns explicitly using model structures. Extensive empirical results show that the new method significantly outperforms the LDA-based methods in term of model quality and meaningfulness of topics and topic hierarchies.

Posted in: Artificial Intelligence , Tagged: , ,