Spatio-temporal maps for mobile robots: taking into account time into the map

June 22, 2017 09:14 ,

João Machado Santos, Tomáš Krajník, Tom Duckett, Spatio-temporal exploration strategies for long-term autonomy of mobile robots, Robotics and Autonomous Systems, Volume 88, February 2017, Pages 116-126, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.11.016.

We present a study of spatio-temporal environment representations and exploration strategies for long-term deployment of mobile robots in real-world, dynamic environments. We propose a new concept for life-long mobile robot spatio-temporal exploration that aims at building, updating and maintaining the environment model during the long-term deployment. The addition of the temporal dimension to the explored space makes the exploration task a never-ending data-gathering process, which we address by application of information-theoretic exploration techniques to world representations that model the uncertainty of environment states as probabilistic functions of time. We evaluate the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months. The combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of continuously changing environments, enabling efficient and self-improving long-term operation of mobile robots.

Posted in: Mobile robot mapping , Tagged: ,

Efficient detection of glass obstacles when using a laser rangefinder

June 22, 2017 09:07 ,

Xun Wang, JianGuo Wang, Detecting glass in Simultaneous Localisation and Mapping, Robotics and Autonomous Systems, Volume 88, February 2017, Pages 97-103, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.11.003.

Simultaneous Localisation and Mapping (SLAM) has become one of key technologies used in advanced robot platform. The current state-of-art indoor SLAM with laser scanning rangefinders can provide accurate realtime localisation and mapping service to mobile robotic platforms such as PR2 robot. In recent years, many modern building designs feature large glass panels as one of the key interior fitting elements, e.g. large glass walls. Due to the transparent nature of glass panels, laser rangefinders are unable to produce accurate readings which causes SLAM functioning incorrectly in these environments. In this paper, we propose a simple and effective solution to identify glass panels based on the specular reflection of laser beams from the glass. Specifically, we use a simple technique to detect the reflected light intensity profile around the normal incident angle to the glass panel. Integrating this glass detection method with an existing SLAM algorithm, our SLAM system is able to detect and localise glass obstacles in realtime. Furthermore, the tests we conducted in two office buildings with a PR2 robot show the proposed method can detect ∼ 95% of all glass panels with no false positive detection. The source code of the modified SLAM with glass detection is released as a open source ROS package along with this paper.

Posted in: Robot sensors , Tagged:

Improving sensory information, diagnosis and fault tolerance by using multiple sensors and sensor fusion, with a good related work section (2.3) on fault tolerance on data fusion

June 22, 2017 09:00 ,

Kaci Bader, Benjamin Lussier, Walter Schön, A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization, Robotics and Autonomous Systems, Volume 88, February 2017, Pages 11-23, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.11.015.

Multisensor perception has an important role in robotics and autonomous systems, providing inputs for critical functions including obstacle detection and localization. It is starting to appear in critical applications such as drones and ADASs (Advanced Driver Assistance Systems). However, this kind of complex system is difficult to validate comprehensively. In this paper we look at multisensor perception systems in relation to an alternative dependability method, namely fault tolerance. We propose an approach for tolerating faults in multisensor data fusion that is based on the more traditional method of duplication–comparison, and that offers detection and recovery services. We detail an example implementation using Kalman filter data fusion for mobile robot localization. We demonstrate its effectiveness in this case study using real data and fault injection.

Posted in: Robot sensors , Tagged: , ,

A very good survey of visual saliency methods, with a list of robotic tasks that have benefit from attention

June 22, 2017 08:56 ,

Ali Borji, Dicky N. Sihite, and Laurent Itti, Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study, IEEE Transactions on Image Processing, V. 22, N. 1, 2013, DOI: 10.1109/TIP.2012.2210727.

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling,
eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.

Posted in: Computer vision , Tagged: , ,

A new method of clustering of data with many advantages w.r.t. others

June 22, 2017 08:44 ,

A. Sharma, K. A. Boroevich, D. Shigemizu, Y. Kamatani, M. Kubo and T. Tsunoda, “Hierarchical Maximum Likelihood Clustering Approach,” in IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 112-122, Jan. 2017. DOI: 10.1109/TBME.2016.2542212.

In this paper, we focused on developing a clustering approach for biological data. In many biological analyses, such as multiomics data analysis and genome-wide association studies analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods: Conventionally, the k-means clustering algorithm is overwhelmingly applied in many areas including biological sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework. Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality. Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood-based methods. Significance: This algorithm uses distribution and centroid information to cluster a sample and was applied to biological data. A MATLAB implementation of this method can be downloaded from the web link http://www.riken.jp/en/research/labs/ims/med_sci_math/.

Posted in: Machine learning , Tagged: , ,

A nice summary of motion planning

June 21, 2017 12:28 ,

J. J. M. Lunenburg, S. A. M. Coenen, G. J. L. Naus, M. J. G. van de Molengraft and M. Steinbuch, “Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms,” in IEEE Robotics & Automation Magazine, vol. 23, no. 4, pp. 107-117, Dec. 2016. DOI: 10.1109/MRA.2015.2510798.

A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice.

Posted in: Robot motion planning , Tagged:

Approach to explain gaze: gaze is directed to task- and goal-relevant scene regions

June 21, 2017 12:12 ,

John M. Henderson, Gaze Control as Prediction, Trends in Cognitive Sciences, Volume 21, Issue 1, January 2017, Pages 15-23, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.11.003.

The recent study of overt attention during complex scene viewing has emphasized explaining gaze behavior in terms of image properties and image salience independently of the viewer’s intentions and understanding of the scene. In this Opinion article, I outline an alternative approach proposing that gaze control in natural scenes can be characterized as the result of knowledge-driven prediction. This view provides a theoretical context for integrating and unifying many of the disparate phenomena observed in active scene viewing, offers the potential for integrating the behavioral study of gaze with the neurobiological study of eye movements, and provides a theoretical framework for bridging gaze control and other related areas of perception and cognition at both computational and neurobiological levels of analysis.

Posted in: Psycho-physiological bases of engineering , Tagged:

Bayesian estimation when computing the likelihood is hard

June 21, 2017 12:04 ,

Kirthevasan Kandasamy, Jeff Schneider, Barnabás Póczos, Query efficient posterior estimation in scientific experiments via Bayesian active learning, Artificial Intelligence, Volume 243, February 2017, Pages 45-56, ISSN 0004-3702, DOI: 10.1016/j.artint.2016.11.002.

A common problem in disciplines of applied Statistics research such as Astrostatistics is of estimating the posterior distribution of relevant parameters. Typically, the likelihoods for such models are computed via expensive experiments such as cosmological simulations of the universe. An urgent challenge in these research domains is to develop methods that can estimate the posterior with few likelihood evaluations.In this paper, we study active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate. Existing techniques for posterior estimation are based on generating samples representative of the posterior. Such methods do not consider efficiency in terms of likelihood evaluations. In order to be query efficient we treat posterior estimation in an active regression framework. We propose two myopic query strategies to choose where to evaluate the likelihood and implement them using Gaussian processes. Via experiments on a series of synthetic and real examples we demonstrate that our approach is significantly more query efficient than existing techniques and other heuristics for posterior estimation.

Posted in: Bayesian filtering

Demonstration that a theory of cortical function (“predictive coding”) can perform bayesian inference in some tasks, with a nice related work of physiological foundations of probability distribution representation in neurons and of bayesian inference

June 21, 2017 11:57 ,

M. W. Spratling, A neural implementation of Bayesian inference based on predictive coding, Connection Science, Volume 28, 2016 – Issue 4, DOI: 10.1080/09540091.2016.1243655.

Predictive coding (PC) is a leading theory of cortical function that has previously been shown to explain a great deal of neurophysiological and psychophysical data. Here it is shown that PC can perform almost exact Bayesian inference when applied to computing with population codes. It is demonstrated that the proposed algorithm, based on PC, can: decode probability distributions encoded as noisy population codes; combine priors with likelihoods to calculate posteriors; perform cue integration and cue segregation; perform function approximation; be extended to perform hierarchical inference; simultaneously represent and reason about multiple stimuli; and perform inference with multi-modal and non-Gaussian probability distributions. PC thus provides a neural network-based method for performing probabilistic computation and provides a simple, yet comprehensive, theory of how the cerebral cortex performs Bayesian inference.

Posted in: Psycho-physiological bases of engineering , Tagged:

Insights into the sparsity of graph-SLAM (i.e., in the smoothing / optimization approach to SLAM) and a good formalization of the problem

June 21, 2017 11:43 ,

K. Khosoussi, S. Huang and G. Dissanayake, “A Sparse Separable SLAM Back-End,” in IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1536-1549, Dec. 2016. DOI: 10.1109/TRO.2016.2609394.

We propose a scalable algorithm to take advantage of the separable structure of simultaneous localization and mapping (SLAM). Separability is an overlooked structure of SLAM that distinguishes it from a generic nonlinear least-squares problem. The standard relative-pose and relative-position measurement models in SLAM are affine with respect to robot and features’ positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of the state variables can be easily computed by solving a sparse linear least-squares problem. We propose an algorithm to exploit this intrinsic property of SLAM by stripping the problem down to its nonlinear core, while maintaining its natural sparsity. Our algorithm can be used in conjunction with any Newton-based solver and is applicable to 2-D/3-D pose-graph and feature-based SLAM. Our results suggest that iteratively solving the nonlinear core of SLAM leads to a fast and reliable convergence as compared to the state-of-the-art sparse back-ends.

Posted in: Mobile robot SLAM , Tagged: