7,511 research outputs found
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
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
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SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles
El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos.
Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap).
La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU
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