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Development of a Laboratory Framework for Testing simultaneous localization and mapping Approaches

IFAC-PapersOnLine 2016年第25期49卷 493-498页

作者： Neduchal, Petr Flídr, Miroslav New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Pilsen Czech Republic

This paper deals with the task of developing a laboratory framework for testing simultaneous localization and mapping algorithms (SLAM). The laboratory framework is composed of three parts. The first one are sensors attached to some mobile device like an unmanned ground or aerial vehicle. The second part is an embedded computer which is employed for collecting data from attached sensors and then it sends it to a remote computing node which is the last part of the proposed laboratory framework. The main purpose of the computing node is to solve the SLAM task. For computation and communication between the embedded computer and the computing node the Robot Operating System (ROS) is used. It provides communication layer based on standard ROS message mechanism that simplifies data exchange over a network. The laboratory framework is designed to work with common combinations of sensors used in various SLAM algorithms. Moreover, the laboratory framework makes it possible to replace the real environment or the robot platform with a simulated alternative. The laboratory framework builds on open source projects and on a proprietary software that is freely available for academic use. © 2016

关键词： Robotics Antennas Cameras Computer operating systems Conformal mapping Electronic data interchange Laboratories Mobile devices Open source software Open systems Sensors Encoders Inertial measurement unit Open source projects Robot operating systems (ROS) Simulated alternatives simultaneous localization and mapping simultaneous localization and mapping algorithms SLAM

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A Tension-based Position Estimation Solution of a Moored Structure and its Uncertain Anchor Positions

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IFAC-PapersOnLine 2016年第23期49卷 251-257页

作者： Ren, Zhengru Skjetne, Roger Department of Marine Technology Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway Department of Marine Technology Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway

Thruster-assisted position mooring (TAPM) is an attractive stationkeeping solution for long-term operation. Due to the complex environmental loads and system structure, increasing attention has been paid to improve the redundancy and reliability. This paper summaries the key research results when introducing the simultaneous localization and mapping algorithm to moored structures, which can provide an additional position reference system with uncertain anchor positions. It is especially cost-efficient for some applications alleviating the need for special sensors, such as, hydroacoustic sensors. The line-of-sight range mapping from tension measurements is discussed. Fairleads, the turret dynamics, and loading effects are considered to provide a more realistic and robust solution. A sensor network scheme and a state-space model are proposed, and an extended Kalman filter (EKF) is employed to estimate the uncertain anchor position. © 2016

关键词： Extended Kalman filters Conformal mapping Mooring Redundancy Robotics Sensor networks State space methods Environmental loads Line of sight ranges Position estimation Position mooring simultaneous localization and mapping algorithms SLAM State space models Tension measurement

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Interactive mobile robot in a dynamic environment1

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IFAC-PapersOnLine 2018年第30期51卷 354-359页

作者： Rehman, Bakhtawar Yagfarov, Rauf Klimchik, Alexandr Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia

In this work we introduced a robotic system to work in a sociable environment alongside humans. The robot is able to recognize information through sound and it is capable to speak in order to response or provide significant information. We implemented and compared different simultaneous localization and mapping (SLAM) algorithms available in Robot Operating System (ROS). We compared their results and showed performance in real time in an indoor environment using lidars as the core sensing module. Thus we came up with a mobile robot autonomously navigating in a dynamic environment capable of interaction and manipulation intelligently. © 2016

关键词： Indoor positioning systems Mobile robots Navigation Robotics Dynamic environments humanoid Indoor environment interaction Interactive control Robot operating systems (ROS) simultaneous localization and mapping algorithms SLAM

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Sparse extended information filters: Insights into sparsification

Sparse extended information filters: Insights into sparsific...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Eustice, R Walter, M Leonard, J Woods Hole Oceanog Inst Dept Appl Ocean Phys & Engn Woods Hole MA 02543 USA

ISBN: (纸本)0780389123

Recently, there have been a number of variant simultaneous localization and mapping (SLAM) algorithms that have made substantial progress towards large-area scalability by parameterizing the SLAM posterior within the information (canonical/inverse covariance) form. Of these, probably the most well-known and popular approach is the Sparse Extended Information Filter (SEIF) by Thrun et al. While SEIFs have been successfully implemented with a variety of challenging real-world datasets and have led to new insights into scalable SLAM, open research questions remain regarding the approximate sparsification procedure and its effect on map error consistency. In this paper, we examine the constant-time SEIF sparsification procedure in depth and offer new insight into issues of consistency. In particular, we show that exaggerated map inconsistency occurs within the global reference frame where estimation is performed, but that empirical testing shows that relative local map relationships are preserved. We then present a slightly modified version of their sparsification procedure, which is shown to preserve sparsity while also generating both local and global map estimates comparable to those obtained by the non-sparsified SLAM filter. While this modified approximation is no longer constant-time, it does serve as a theoretical benchmark against which to compare SEIFs constant-time results. We demonstrate our findings by benchmark comparison of the modified and original SEIF sparsification rule using simulation in the linear Gaussian SLAM case and real-world experiments for a nonlinear dataset.

关键词： intelligent robots position control SLAM algorithms linear Gaussian SLAM map error consistency simultaneous localization and mapping algorithms sparse extended information filters sparsification Intelligent robots Position control Web filters REFERENCE FRAMES maps simultaneous localization and mapping Modified

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A mapping Method Tolerant to Calibration and localization Errors Based on Tilting 2D Laser Scanner

A Mapping Method Tolerant to Calibration and Localization Er...

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European Control Conference (ECC)

作者： Kolu, Antti Lauri, Mikko Hyvonen, Mika Ghabcheloo, Reza Huhtala, Kalevi Tampere Univ Technol Dept Intelligent Hydraul & Automat FIN-33101 Tampere Finland Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland

ISBN: (纸本)9783952426937

Autonomous mobile machines use onboard sensors for navigation and obstacle avoidance. The accuracy of the sensor data in global frame is however dependent on the localization accuracy of the machine. simultaneous localization and mapping algorithms (SLAM) are widely used with 3D laser scanners for mapping the world. They use scan matching algorithms to solve the accuracy problem by matching prior sensor data of the environment with the newly acquired data. However matching scans is not always possible. Insufficient amount of prior data or too few features in the scan can prevent the scan matching algorithm from finding a match. Thus it is important that also the mapping algorithm is tolerant to some degree of error in localization and calibration. We present a method for generating obstacle maps from smaller data segments at a time, thus making the mapping system more tolerant to navigation and calibration errors. The obstacle mapping method is tested with modified Avant multipurpose loader.

关键词： SLAM (robots) calibration collision avoidance image matching optical scanners robot vision sensors 2D laser scanner tilting 3D laser scanners SLAM autonomous mobile machines calibration errors localization errors mapping method tolerant modified Avant multipurpose loader navigation errors obstacle avoidance obstacle mapping method onboard sensors scan matching algorithms sensor data matching simultaneous localization and mapping algorithms Accuracy Calibration Navigation Servomotors simultaneous localization and mapping Three-dimensional displays

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