Robust robot localization in a complex oil and gas industrial environment

Merriaux, Pierre; Dupuis, Yohan; Boutteau, Rémi; Vasseur, Pascal; Savatier, Xavier

doi:10.1002/rob.21735

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…In the future, the current work can be fulfilled in multiple directions. Some limitations of the proposed method have already been discussed, we believe that utilizing a more robust measurement model, such as likelihood field model [41] or normal distribution representation [42], may be a possible solution for enhancing the localization accuracy. Also, the pose estimation can be extended to 6 DoF that the usage of 3D LIDAR sensors will be involved in order to improve the robustness against the point cloud map.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

Wang

Kobayashi

Ravankar

et al. 2019

Sensors

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Scale ambiguity and drift are inherent drawbacks of a pure-visual monocular simultaneous localization and mapping (SLAM) system. This problem could be a crucial challenge for other robots with range sensors to perform localization in a map previously built by a monocular camera. In this paper, a metrically inconsistent priori map is made by the monocular SLAM that is subsequently used to perform localization on another robot only using a laser range finder (LRF). To tackle the problem of the metric inconsistency, this paper proposes a 2D-LRF-based localization algorithm which allows the robot to locate itself and resolve the scale of the local map simultaneously. To align the data from 2D LRF to the map, 2D structures are extracted from the 3D point cloud map obtained by the visual SLAM process. Next, a modified Monte Carlo localization (MCL) approach is proposed to estimate the robot’s state which is composed of both the robot’s pose and map’s relative scale. Finally, the effectiveness of the proposed system is demonstrated in the experiments on a public benchmark dataset as well as in a real-world scenario. The experimental results indicate that the proposed method is able to globally localize the robot in real-time. Additionally, even in a badly drifted map, the successful localization can also be achieved.

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Section: Discussionmentioning

confidence: 99%

A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

Wang

Kobayashi

Ravankar

et al. 2019

Sensors

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“…In our previous work [27] a novel localization method is proposed for robot localization in ambiguous environments, which is based on a pre-built map named ambiguity grid map that models the ambiguous property of an environment. To realize robust localization in an oil and gas industrial environment, a pre-computed 3d likelihood field using hybrid octree is presented by Merriaux et al and efficiency likelihood computation can be obtained through this map [28]. By employing multi-layer maps, Vasiljevic et al achieve a robust forklifts localization in a dynamic industrial environment with the MCL method [29].…”

Section: Maps For Robot Localizationmentioning

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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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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“…The device-free localization (DFL) technology [2], a kind of wireless localization technology, has been therefore acknowledged as an emerging technology for providing high quality-of-service (QoS) in the Internet-of-Things (IoT) environment [3]. Because DFL can locate targets without carrying any attached devices or tags [4], it has spawned a variety of emerging industrial and household applications, such as intrusion detection in security safeguard [5], mobile robot localization [6] in smart factories, and healthcare monitoring of patients and the elderly [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Zhao

Huang

et al. 2021

IEEE Internet Things J.

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Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds -10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

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Robust robot localization in a complex oil and gas industrial environment

Cited by 20 publications

References 21 publications

A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

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

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

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