High-accuracy vehicle localization for autonomous warehousing

Vasiljević, Goran; Miklić, Damjan; Draganjac, Ivica; Kovačić, Zdenko; Lista, Paolo

doi:10.1016/j.rcim.2016.05.001

Cited by 67 publications

(31 citation statements)

References 21 publications

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“…They are able to achieve average localization error and positioning error in a laboratory static environment within 3mm/0.06deg and 5 mm/0.15 deg respectively. By using the discrete Fourier transform, Vasiljević et al develop an accuracy pose tracking method for autonomous warehousing in an industrial setting whose positioning accuracy reached 1.5 cm and 0.5 deg [3]. Although accuracy pose tracking can be achieved through those methods, however, without explicitly considering the negative impacts of a harsh manufacturing workshop, their localization method may be hard to apply to the selflocalization of mobile manipulators.…”

Section: Pose Trackingmentioning

confidence: 99%

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Hybrid Maps Enhanced Localization System for Mobile Manipulator in Harsh Manufacturing Workshop

Huang

Zhang

et al. 2020

IEEE Access

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With excellent mobility and flexibility, mobile manipulators have great potential for loading and unloading tasks of numerical control machine tools (CNC) in manufacturing workshops. However, because of the rough and oily ground, dynamic obstacles and the convex plate of a CNC, harsh manufacturing workshop poses a huge challenge to the localization system of an autonomous mobile manipulator. To address the above problem, this paper presents a hybrid maps enhanced localization system which mainly consists of a global localization method and a pose tracking method. Hybrid maps including hybrid grid map, multiresolution likelihood fields (MLFs) and hybrid point map are constructed to efficaciously model the harsh environment and to improve localization performance. Our global localization method employs the convex hull sampling to spares dense Lidar data and the MLFs based branch and bound (BnB) search to speed up global search. To achieve real-time localization reliably and accurately, our pose tracking method seamlessly combines the BnB search and the adaptive Monte Carlo localization, and the Iterative Closest Point (ICP) based scan matching using the hybrid point map is adopted for higher accuracy. In addition, a distance filter improved by unscented transform is integrated into the pose tracking process to mitigate the influence of dynamic obstacles. The developed localization system is evaluated through different experiments including two weeks of loading and unloading tasks in a real manufacturing scenario, resulting in superior localization performance.

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Section: Pose Trackingmentioning

confidence: 99%

“…Therefore, it is crucial to achieve an accurate and reliable localization for the mobile platform. Some works have been done for robot localization in industry environments [3]- [5]. In their implementation, 2D Lidar is the key sensor due to its high frequency, high accuracy, low cost and small data size.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Maps Enhanced Localization System for Mobile Manipulator in Harsh Manufacturing Workshop

Huang

Zhang

et al. 2020

IEEE Access

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“…A solution using an initial reference scan of the environment using 3D LiDAR scans is presented in [10]. Similarily, [14] and [15] use scan matching and MCL in 2D using 2D LiDAR sensors. However, no domain shift in localization between environment model and LiDAR scans is performed, as the work location is inserted into the environment represented by the reference scan.…”

Section: Related Workmentioning

confidence: 99%

A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction

Gawel

Siegwart

Hutter

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites. The approach leverages multi-modal sensing capabilities for state estimation, tight integration with digital building models, and integrated trajectory planning and whole-body motion control. A novel method for high-accuracy localization updates relative to the known building structure is proposed. The approach is implemented on a real platform and tested under realistic construction conditions. We show that the system can achieve sub-cm end-effector positioning accuracy during fully autonomous operation using solely onboard sensing. contributed to the state estimation and high accuracy localization {blumh, gawela, rsiegwart}@ethz.ch. Timothy Sandy contributed to the state estimator, and motion control tsandy@ethz.ch. Koen Krämer, Johannes Pankert, Farbod Farshidian, Marco Hutter contributed to the Motion planning and control {kokraeme, pankertj, farshidian, mahutter}@ethz.ch. Selen Ercan, Fabio Gramazio contributed to the building task interface {ercan, gramazio}@arch.ethz.ch.

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“…It seems that scientific results published to date neither provide sufficient accuracy for industrial applications nor have they been extensively tested in realistic, industrial-like operating conditions [10]. In fact, the recognition performance of machine vision systems is susceptible to external environments, especially the surrounding illumination [11,12].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

Sun

Zou

et al. 2019

Applied Sciences

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Applying computer vision to mobile robot navigation has been studied more than twodecades. The most challenging problems for a vision-based AGV running in a complex workspaceinvolve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably resultin incomplete or deformed path images as well as many fake artifacts. Neither the fixed thresholdmethods nor the iterative optimal threshold methods can obtain a suitable threshold for the pathimages acquired on all conditions. It is still an open question to estimate the model parameters ofguide paths accurately by distinguishing the actual path pixels from the under- or oversegmentationerror points. Hence, an intelligent path recognition approach based on KPCA–BPNNand IPSO–BTGWP is proposed here, in order to resist the interferences from the complexworkspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of apath classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP wasdeveloped for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robustpath estimation method based on IPSO was proposed to figure out the path parameters from a set ofpath pixels surrounded by noise points. Experimental results showed that our approach caneffectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in acomplex workspace.

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High-accuracy vehicle localization for autonomous warehousing

Cited by 67 publications

References 21 publications

Hybrid Maps Enhanced Localization System for Mobile Manipulator in Harsh Manufacturing Workshop

Hybrid Maps Enhanced Localization System for Mobile Manipulator in Harsh Manufacturing Workshop

A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

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