A Large Scale Inertial Aided Visual Simultaneous Localization and Mapping (SLAM) System for Small Mobile Platforms PDF Download

Author: Ashraf Qadir
Publisher:
ISBN: 9781369183443
Category : Autonomous robots
Languages : en
Pages : 234

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Author: Daniel Asmar
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Andrey Ronzhin
Publisher: Springer
ISBN: 3319995820
Category : Computers
Languages : en
Pages : 312

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Book Description
This book constitutes the proceedings of the Third International Conference on Interactive Collaborative Robotics, ICR 2018, held in Leipzig, Germany, in September 2018, as a satellite event of the 20th International Conference on Speech and Computer, SPECOM 2018. The 30 papers presented in this volume were carefully reviewed and selected from 51 submissions. The papers presents challenges of human-robot interaction, robot control and behavior in social robotics and collaborative robotics, as well as applied robotic and cyberphysical systems.

Author: Zhan Wang
Publisher: World Scientific
ISBN: 9814460486
Category : Computers
Languages : en
Pages : 208

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Book Description
Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

Author: Jianzhu Huai
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Navigation from one place to another involves estimating location and orientation. As such, navigation and location-based services are essential to our daily lives. Prevalent navigation techniques rely on integrating global navigation satellite systems (GNSS) and inertial navigation systems (INS) which does not provide reliable and accurate solutions when GNSS signals are degraded and in the indoor environments. To remedy this deficiency, simultaneous localization and mapping (SLAM) approaches have been developed for navigation purposes. However, SLAM uses data from one device or one session, and is limited in the amount of available information and thus is prone to accumulating errors. Using data from multiple sessions or devices, a collaborative SLAM technique holds promise for reusing mapping information and enhancing localization accuracy. Such techniques gain further significance in view of the widespread cloud computing resources and handheld mobile devices. With crowdsourced data from these consumer devices, collaborative SLAM is key to many location-based services, e.g., navigating a building for a group of people or robots. However, available solutions and scope of research investigations are somewhat limited in this field. Therefore, this work focuses on solving the collaborative SLAM problem with crowdsourced data. This problem is approached at three aspects in this work: collaborative SLAM with crowdsourced visual data, calibrating a camera-IMU (inertial measurement unit) sensor system found on mobile devices, and collaborative SLAM with crowdsourced visual and inertial data. For the first aspect, a collaborative SLAM framework is proposed based on the client-server architecture. It has independent clients estimating the device motion using the visual data from mobile devices. The output from these clients is processed by the server to collaboratively map the environment and to refine estimates of device motion which are transmitted to clients to update their estimates. The proposed framework achieves reuse of the existing maps, robust loop closure, and real-time collaborative and accurate mapping. These properties of the framework were validated with experiments on a benchmark dataset and visual data crowdsourced by smartphones. For the second aspect, a calibration approach based on the extended Kalman filter (EKF) is developed for the camera-IMU system of a mobile device. Aimed at target-free on-the-fly calibration, this approach estimates the intrinsic parameters of both the camera and IMU sensor, and the temporal and spatial relations between the two sensors. Simulation and experiments with a benchmark dataset and smartphone data validated the calibration performance. The calibration approach provides essential parameters for integrating visual and inertial data which is the highlight of the third aspect. There the collaborative SLAM framework is extended to work with visual and inertial data. While keeping such features as reusing the existing maps and real-time collaborative mapping, the new framework has the metric scale largely observable thanks to the inertial data. These features of the framework were validated with experiments on visual and inertial data collected by smartphones. In summary, these research efforts have proved that accurate and effective collaborative SLAM is achievable with crowdsourced data at the level that has not been demonstrated before. It represents a step towards location-based services which harness the power of crowdsourcing, such as crowd-based gaming and emergency response.

Author: Michael Montemerlo
Publisher: Springer
ISBN: 3540464026
Category : Technology & Engineering
Languages : en
Pages : 129

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Book Description
This monograph describes a new family of algorithms for the simultaneous localization and mapping (SLAM) problem in robotics, called FastSLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including a solution to the problem of people tracking.

Author: Dominik Schlegel
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: João Pedro Machado dos Santos
Publisher: University of Coimbra
ISBN:
Category :
Languages : en
Pages : 88

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Book Description
Simultaneous Localization and Mapping (SLAM) is one of the most widely researched topics in Robotics. It addresses building and maintaining maps within unknown environments, while the robot keeps the information about its location. It is a basic requirement for autonomous mobile robotic navigation in many scenarios, including military applications, search and rescue, environmental monitoring, etc. Although SLAM techniques have evolved considerably in the last years, there are many situations which are not easily handled, such as the case of smoky environments where commonly used range sensors for SLAM, like Laser Range Finders (LRF) and cameras, are highly disturbed by noise induced in the measurement process by particles of smoke. There is an evident lack of solutions to this issue in the literature. This work focuses on SLAM techniques for reduced visibility scenarios. The main objective of this work is to develop and validate a SLAM technique for those scenarios, using dissimilar range sensors and by evaluating their behavior in such conditions. To that end, a study of several laser-based 2D SLAM techniques available in Robot Operating System (ROS) is firstly conducted. All the tested approaches are evaluated and compared in 2D simulations as well as real world experiments using a mobile robot. Such analysis is fundamental to decide which technique to adopt according to the final application of the work. The developed technique uses the complementary characteristics between a LRF and an array of sonars in order to successfully map the aforementioned environments. In order to validate the developed technique , several experimental tests were conducted using a real scenario. It was verified that this approach is adequate to decrease the impact of smoke particles in the mapping task. However, due to hardware limitations, the resulting map is comprehensibly not outstanding, but much better than using a single range sensor modality. This work is part of the Cooperation between Human and rObotic teams in catastroPhic INcidents (CHOPIN) R&D project, which intends to develop a support system for small scale SaR missions in urban catastrophic scenarios by exploiting the human-robot symbiosis.

Author: Seth C. Tucker
Publisher:
ISBN:
Category :
Languages : en
Pages : 124

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Book Description
Monocular simultaneous localization and mapping (SLAM) is an important technique that enables very inexpensive environment mapping and pose estimation in small systems such as smart phones and unmanned aerial vehicles. However, the information generated by monocular SLAM is in an arbitrary and unobservable scale, leading to drift and making it difficult to use with other sources of odometry for control or navigation. To correct this, the odometry needs to be aligned with metric scale odometry from another device, or else scale must be recovered from known features in the environment. Typically known environmental features are not available, and for systems such as cellphones or unmanned aerial vehicles (UAV), which may experience sustained, small scale, irregular motion, an IMU is often the only practical option. Because accelerometers measure acceleration and gravity, an inertial measurement unit (IMU) must filter out gravity and track orientation with complex algorithms in order to provide a linear acceleration measurement that can be used to recover SLAM scale. In this thesis, an alternative method will be proposed, which detects and removes gravity from the accelerometer measurement by using the unscaled direction of acceleration derived from the SLAM odometry.

Author: Yi Ma
Publisher: Springer Science & Business Media
ISBN: 0387217797
Category : Computers
Languages : en
Pages : 542

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Book Description
This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.