Towards terrestrial 3D data registration improved by parallel programming and evaluated with geodetic precision

Bedkowski, Janusz; Majek, Karol; Musialik, Pawel; Adamek, A.; Andrzejewski, Dariusz; Czekaj, Damian

doi:10.1016/j.autcon.2014.07.013

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…In this paper we show current research results of 6D SLAM from the application point of view, thus input data for the qualitative reasoning are based on accurate 3D models of the environment. We achieve better accuracy of final 3D metric map compared to our previous work [3], where we designed ground truth 3D data setup measured with geodetic precision. For spatial design purpose we create a QSTRR model based on this accurate 3D metric map.…”

Section: Introductionmentioning

confidence: 92%

Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings

Bedkowski

Majek

et al. 2015

Mobile Netw Appl

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This paper concerns an intelligent mobile application for spatial design support and security domain. Mobility has two aspects in our research: The first one is the usage of mobile robots for 3D mapping of urban areas and for performing some specific tasks. The second mobility aspect is related with a novel Software as a Service system that allows access to robotic functionalities and data over the Ethernet, thus we demonstrate the use of the novel NVIDIA GRID technology allowing to virtualize the graphic processing unit. We introduce Complex Shape Histogram, a core component of our artificial intelligence engine, used for classifying 3D point clouds with a Support Vector Machine. We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm. Our intelligent mobile system is built on top of the Qualitative Spatio-Temporal Representation and Reasoning framework. This framework defines an ontology and a semantic model, which are used for building the intelligent mobile user interfaces. We show experiments demonstrating advantages of our approach. In addition, we test our prototypes in the field after the end-user case studies demonstrating a relevant contribution for future intelligent mobile systems that merge mobile robots with novel data centers.

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Section: Introductionmentioning

confidence: 92%

Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings

Bedkowski

Majek

et al. 2015

Mobile Netw Appl

Self Cite

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“…31 The 6-DOF SLAM includes the following six dimensions: the mobile robot's 3D position coordinates (x, y, z) and the angular orientation data (yaw, pitch, roll), and an extended Kalman filter (EKF) is used for sensor fusion, neural field dynamics used for behavior generation, and evolutionary algorithms used to optimize each part of the system. [32][33][34][35] We propose the novel IICP algorithm that uses visual features and their associated depth values to obtain an initial alignment. A SIFT detects the features, random sampling consensus (RANSAC) matches the data, and least-square is used to fit 3D points.…”

Section: Improved Icps For Data Associationmentioning

confidence: 99%

Three-Dimensional Reconstruction Based on Visual SLAM of Mobile Robot in Search and Rescue Disaster Scenarios

Wang¹,

Zhang

Song

et al. 2019

Robotica

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SummaryConventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.

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“…By simulating the microscopic behavior of physical systems, the macroscopic equation of the LB method can match certain PDEs. The idea of the LB method is to transpose discrete dynamics for simulating the macroscopic model described by a PDE, using densities of particles colliding and streaming on a regular lattice [3]. The density of each lattice node follows the same simple evolution rules, in which only each node of the lattice and its neighbors are involved.…”

Section: Introductionmentioning

confidence: 99%

“…Inherent parallelism is parallelism that occurs naturally within an algorithm, not as a result of any special effort on the part of the algorithm or machine designer. For example, in [3], the three-dimensional data registration is improved by parallel programming for solving the simultaneous localization and mapping problem of the robot motion model. The main idea is to parallize the NNS (the nearest neighbor search) computation, which is the first step Information 2020, 11, 1 2 of 22 of three-dimensional data registration.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Chen

Courbebaisse

2019

Information

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Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

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Towards terrestrial 3D data registration improved by parallel programming and evaluated with geodetic precision

Cited by 9 publications

References 9 publications

Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings

Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings

Three-Dimensional Reconstruction Based on Visual SLAM of Mobile Robot in Search and Rescue Disaster Scenarios

A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

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