Robust Vision-Based Relative-Localization Approach Using an RGB-Depth Camera and LiDAR Sensor Fusion

Song, Hyoung Woon; Choi, Won-Sub; Kim, Hae-Dong

doi:10.1109/tie.2016.2521346

Cited by 96 publications

(33 citation statements)

References 24 publications

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“…Laser range finder [5] was installed on mobile robot to localize in environment where glass walls surrounded. RGB-depth camera in the light of two-dimensional light detection and ranging technique was used for localization in [6]. In contrast to common device-enable technology [7], device-free technology Manuscript received August 9, 2017; revised November 17, 2017; accepted December 6, 2017.…”

Section: Introductionmentioning

confidence: 99%

Indoor Sound Source Localization With Probabilistic Neural Network

Sun

Chen

Yuen

et al. 2018

IEEE Trans. Ind. Electron.

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It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Generalized cross correlation Classification Algorithm (GCA). Experimental results for adverse environments with high reverberation time T60 up to 600ms and low SNR such as -10dB show that, the average azimuth angle error and elevation angle error by GCA are only 4.6º and 3.1º respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage. Index Terms-Sound source localization (SSL), direction of arrival (DOA), generalized cross correlation (GCC), probabilistic neural network (PNN), machine learning.

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Section: Introductionmentioning

confidence: 99%

Indoor Sound Source Localization With Probabilistic Neural Network

Sun

Chen

Yuen

et al. 2018

IEEE Trans. Ind. Electron.

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“…One issue associated with this approach is the challenge in matching 2D images to 3D cloud points. Reflection intensity data from commercial LiDARs measurements can be exploited directly for crack and defect quantification . With two brick and two concrete buildings, Laefer et al compared crack detection methods including manual inspection, digital photography and LiDAR.…”

Section: Introductionmentioning

confidence: 99%

“…Reflection intensity data from commercial LiDARs measurements can be exploited directly for crack and defect quantification. 28,29 With two brick and two concrete buildings, Laefer et al 30 compared crack detection methods including manual inspection, digital photography and LiDAR. Engineers who adopt the manual inspection method, a common approach in ATC-20, 2 identify cracks with naked eyes.…”

Section: Introductionmentioning

confidence: 99%

Surface damage quantification of postearthquake building based on terrestrial laser scan data

Dai

Zhang

et al. 2018

Struct Control Health Monit

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Summary Light Detection and Ranging (LiDAR) has been suggested as a remote sensing tool for condition monitoring of structures. The laser‐generated point cloud data contains 3‐dimensional (3D) coordinate information of the object of interest, which can be processed for structural assessment. Commercial LiDARs also report the reflection intensity, representing the scattering of laser energy on the object surface. This paper proposes an innovative technique to enhance the structure damage quantification by integrating LiDAR point cloud 3D coordinate data and reflection intensity data using photogrammetric techniques. Unlike existing methods, the proposed algorithm simultaneously uses both 3D coordinate and reflection intensity information to improve the accuracy of damage assessment results. LiDAR scan data of an earthquake damaged building was used to demonstrate the damage assessment method of a wall component. The study indicates that the proposed method can significantly improve on the quantification of surface damage of the structure component.

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“…These are noninvasive sensing methods and are appropriate for indoor applications. Well-established studies of DFL employ distributed cameras [7], depth imaging sensors [8] and pyroelectric infrared (PIR) sensors [9] to detect and obtain human position. However, these optical sensing-based methods are limited by smoke, varying illumination, blind field-of-view (FOV), and the occlusion of obstacles.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

Liu

Luo

Liang

2018

JSAN

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Abstract:The sparse distribution of targets in monitored areas is an important prior for device-free localization (DFL) with radio tomography networks. In this article, our goal is to develop an enhanced sparse representation-based DFL method that takes the full potential of sparsity for location reconstruction. An expanded sensing matrix spanning the concatenation of a sampling matrix and a unit error-correcting base is proposed for modelling the measurement process. The sampling matrix can either be composed of the ellipse model from calibrated networks or the received signal strength (RSS) fingerprint-based model induced by training samples with one person at predefined locations. Thus, the sparsity of targets is enhanced under the expanded sensing matrix and the 1 -minimization-based approximations are derived for the recovery of locations. Experimental studies in an open outdoor scenario, in a line-of-sight (LOS) indoor scenario, and in a non-line-of-sight (NLOS) indoor scenario, are conducted to verify the efficacy of the proposed method.

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Robust Vision-Based Relative-Localization Approach Using an RGB-Depth Camera and LiDAR Sensor Fusion

Cited by 96 publications

References 24 publications

Indoor Sound Source Localization With Probabilistic Neural Network

Indoor Sound Source Localization With Probabilistic Neural Network

Surface damage quantification of postearthquake building based on terrestrial laser scan data

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

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