Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Jovanźeviź, Igor; Viana, Ilisio; Orteu, Jean-José; Sentenac, Thierry; Larnier, Stanislas

doi:10.5220/0005756303590366

Cited by 9 publications

(7 citation statements)

References 18 publications

(14 reference statements)

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“…It is a practical wisdom to implement more general structure prior information of the environment to improve the localization and mapping quality rather than to consider the environment as entirely unknown. For instance, in a building inspection environment, the structure information, as elaborate as the computer‐aided design models, or as coarse as some common sense such as flat planes, parallel lines, with proper parameterization, can be implemented to aid the localization process (Hasan et al, 2017; Jovančević et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

SPINS: A structure priors aided inertial navigation system

Lyu

Liu

Cao

et al. 2023

Journal of Field Robotics

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We propose a navigation system combining sensor‐aided inertial navigation and prior‐map‐based localization to improve the stability and accuracy of robot localization in structure‐rich environments. Specifically, we adopt point, line, and plane features in the navigation system to enhance the feature richness in low‐texture environments and improve the localization reliability. We additionally integrate structure prior information of the environments to constrain the localization drifts and improve the accuracy. The prior information is called structure priors and parameterized as low‐dimensional relative distances/angles between different geometric primitives. The localization is formulated as a graph‐based optimization problem that contains sliding‐window‐based variables and factors, including Inertial Measurement Unit, heterogeneous features, and structure priors. A limited number of structure priors are selected based on the information gain to alleviate the computation burden. Finally, the proposed framework is extensively tested on synthetic data, public data sets, and, more importantly, on the real Unmanned Aerial Vehicle flight data obtained from both indoor and outdoor inspection tasks. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

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Section: Related Workmentioning

confidence: 99%

SPINS: A structure priors aided inertial navigation system

Lyu

Liu

Cao

et al. 2023

Journal of Field Robotics

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show abstract

“…It is practical wisdom to implement more general structure prior information of the environment to improve the localization and mapping quality rather than to consider the environment as entirely unknown. For instance, in a building inspection environment, the structure information, as elaborate as the CAD modes, or as coarse as some common sense such as flat planes, parallel lines, with proper parameterization, can be implemented to aid the SLAM process [21], [39].…”

Section: Related Workmentioning

confidence: 99%

SPINS: Structure Priors aided Inertial Navigation System

Lyu¹,

Liu

Cao³

et al. 2020

Preprint

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Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robustness. More importantly, we incorporate general prior map information into the SLAM to restrain its drift and improve the accuracy. To avoid rigorous association between prior information and local observations, we parameterize the prior knowledge as low dimensional structural priors defined as relative distances/angles between different geometric primitives. The localization is formulated as a graphbased optimization problem that contains sliding-window-based variables and factors, including IMU, heterogeneous features, and structure priors. We also derive the analytical expressions of Jacobians of different factors to avoid the automatic differentiation overhead. To further alleviate the computation burden of incorporating structural prior factors, a selection mechanism is adopted based on the so-called information gain to incorporate only the most effective structure priors in the graph optimization. Finally, the proposed framework is extensively tested on synthetic data, public datasets, and, more importantly, on the real UAV flight data obtained from a building inspection task. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

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“…Present similarity measure for segments is modified to ellipses. This comparison enables the detection and data association processes for navigation and inspection tasks on aircraft parts (Jovančević et al, 2016). Roberson et al (2013) developed a decision making and ranking model for selecting an appropriate 3D printer using Deng's Similarity approach based on accuracy, printing time and product surface smoothness.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection on 3D Print Products and in Concrete Structures Using Image Processing and Convolution Neural Network

Garfo¹,

Muktadir²,

Sun³

2020

Journal of Mechatronics and Robotics

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This paper explores the automated detection of surface defects on 3-D printed products and concrete structures. They are the main factors to evaluate their quality in addition to dimension and roughness. Traditional detection by human inspectors is far from satisfactory. Manual inspection is time-consuming, error-prone and often leads to loss of resources. For this purpose, image processing and deep learning-based object detection adopted by Google Cloud Machine Learning (ML) Engine is used to detect surface defects. In the case of image processing, two approaches are presented in this paper. In both cases, pixels are being considered to differentiate a smooth or rough surface from a picture taken by a USB camera. For the deep learning-based solution, MobileNet -a base convolution neural network treated as an image feature extractor in combination with Single Shot MultiBox Detector (SSD) as an object detector hence MobileNet-SSD. The model was successfully trained on the Google Cloud ML Engine with the dataset of 20000+ images. The review of the results confirms that with the help of MobileNet-SSD can automatically detect surface defects more accurately and rapidly than conventional deep learning methods.

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Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Cited by 9 publications

References 18 publications

SPINS: A structure priors aided inertial navigation system

SPINS: A structure priors aided inertial navigation system

SPINS: Structure Priors aided Inertial Navigation System

Defect Detection on 3D Print Products and in Concrete Structures Using Image Processing and Convolution Neural Network

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