Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Intelligent computational sketching support for conceptual design

Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Building an environment model using depth information

Modeling the environment is one of the most crucial issues for the development and research of autonomous robot and tele-perception. Though the physical robot operates (navigates and performs various tasks) in the real world, any type of reasoning, such as situation assessment, planning or reasoning about action, is performed based on information in its internal world. Hence, the robot's intentional actions are inherently constrained by the models it has. These models may serve as interfaces between sensing modules and reasoning modules, or in the case of telerobots serve as interface between the human operator and the distant robot. A robot operating in a known restricted environment may have a priori knowledge of its whole possible work domain, which will be assimilated in its World Model. As the information in the World Model is relatively fixed, an Environment Model must be introduced to cope with the changes in the environment and to allow exploring entirely new domains. Introduced here is an algorithm that uses dense range data collected at various positions in the environment to refine and update or generate a 3-D volumetric model of an environment. The model, which is intended for autonomous robot navigation and tele-perception, consists of cubic voxels with the possible attributes: Void, Full, and Unknown. Experimental results from simulations of range data in synthetic environments are given. The quality of the results show great promise for dealing with noisy input data. The performance measures for the algorithm are defined, and quantitative results for noisy data and positional uncertainty are presented

Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project

Proyecto de Excelencia Junta de Andalucía TEP2012-530Olfaction is a valuable source of information about the environment that has not been su ciently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efciency, autonomy and usefulness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec