Application of Simultaneous Localization and Mapping for Large-scale Manipulators in Unknown Environments

Mäkinen, Petri; Aref, Mohammad M.; Mattila, Jouni; Launis, Sirpa

doi:10.1109/cis-ram47153.2019.9095770

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…However, for the automation of large earth moving machines few efforts have been made to enable robust pose estimation for operation in challenging environments. Current works, such as [16,17], have tried to directly apply existing methods on construction machines to enable automation for specific tasks, however, for general applications recent works have emphasized on the design of specific and flexible approaches to enable general autonomy for excavators [18]. In particular, [19,20] present a comprehensive review of the state-of-the-art in constructions robotics and stress on the utilization of multi-sensor perception and fusion to enable operation in complex environments.…”

Section: Related Workmentioning

confidence: 99%

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

Nubert

Khattak

Hutter

2022

2022 International Conference on Robotics and Automation (ICRA)

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Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core component to enable these machines for operation in a diverse set of complex environments. In this work, a method for multimodal sensor fusion for robot state-estimation and localization is presented, enabling operation of construction robots in realworld scenarios. The proposed approach presents a graph-based prediction-update loop that combines the benefits of filtering and smoothing in order to provide consistent state estimates at high update rate, while maintaining accurate global localization for large-scale earth-moving excavators. Furthermore, the proposed approach enables a flexible integration of asynchronous sensor measurements and provides consistent pose estimates even during phases of sensor dropout. For this purpose, a dualgraph design for switching between two distinct optimization problems is proposed, directly addressing temporary failure and the subsequent return of global position estimates. The proposed approach is implemented on-board two Menzi Muck walking excavators and validated during real-world tests conducted in representative operational environments.

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Section: Related Workmentioning

confidence: 99%

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

Nubert

Khattak

Hutter

2022

2022 International Conference on Robotics and Automation (ICRA)

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“…The initial conditions for both observers were set to p(0) = [−2 0 7] T , R(0) = R(π/4, e 1 ), and η = 0.4 * η. Figures (1)(2)(3)(4) illustrate the results of this experiment. Figure (1) depicts the estimated paths of the robot's position and the landmarks' positions versus time, as well as the actual robot path and landmark positions.…”

Section: A First Experimentsmentioning

confidence: 99%

“…Figures (1)(2)(3)(4) illustrate the results of this experiment. Figure (1) depicts the estimated paths of the robot's position and the landmarks' positions versus time, as well as the actual robot path and landmark positions. The evolution of the attitude tracking error and position tracking error are shown in Figure (2).…”

Section: A First Experimentsmentioning

confidence: 99%

A Global Asymptotic Convergent Observer for SLAM

Hashemi¹,

Mattila²

2022

Preprint

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This paper examines the global convergence problem of SLAM algorithms, an issue that faces topological obstructions. This is because the state-space of attitude dynamics is defined on a non-contractible manifold: the special orthogonal group of order three SO(3). Therefore, this paper presents a novel, gradient-based hybrid observer to overcome these topological obstacles. The Lyapunov stability theorem is used to prove the globally asymptotic convergence of the proposed algorithm. Finally, comparative analyses of two simulations were conducted to evaluate the performance of the proposed scheme and to demonstrate the superiority of the proposed hybrid observer to a smooth observer.

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“…S IMULTANEOUS Localization and Mapping (SLAM) is a well-known highly nonlinear problem which many previous studies have examined [1]. This estimation problem has an extensive variety of applications, ranging from unmanned aerial vehicles (UAV) to underwater robotics.…”

Section: Introduction a Motivation And Problem Statementmentioning

confidence: 99%

Global Asymptotic Convergent Observer for SLAM

Hashemi

Mattila

2022

IEEE Access

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This paper examines the global convergence problem of SLAM algorithms, an issue that faces topological obstructions. This is because the state-space for attitude kinematics is defined on a noncontractible manifold: the special orthogonal group of order three SO(3). Therefore, this paper presents a novel, gradient-based hybrid observer to overcome these topological obstacles. Moreover, the integral action is applied into the proposed observer to estimate unknown constant bias, which results in an increase in bias estimation. Accordingly, a new projection scheme is defined to cope with the integral action. The Lyapunov stability theorem is used to prove the globally asymptotic convergence of the proposed algorithm. Experimental and simulation results are provided to evaluate the performance and to demonstrate the effectiveness and robustness of the proposed scheme.

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Application of Simultaneous Localization and Mapping for Large-scale Manipulators in Unknown Environments

Cited by 6 publications

References 19 publications

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

A Global Asymptotic Convergent Observer for SLAM

Global Asymptotic Convergent Observer for SLAM

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