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

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

A decentralized motion coordination strategy for dynamic target tracking

This paper presents a decentralized motion planning algorithm for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets. Our gradient-descent method is based on a cost function that measures the overall quality of sensing. We also investigate the role of imperfect communication between sensor agents in this framework, and examine the trade-offs in performance between sensing and communication. Simulations illustrate the basic characteristics of the algorithms

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018