L-ICPSnet: LiDAR Indoor Camera Positioning System for RGB to Point Cloud Translation using End2End Generative Network

Ghofrani, Ali; Mahdian, Rahil; Tabatabaie, Seyed Mojtaba; Tabasi, Seyed Maziyar

doi:10.1109/cfis49607.2020.9238706

2020 8th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) 2020

DOI: 10.1109/cfis49607.2020.9238706

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L-ICPSnet: LiDAR Indoor Camera Positioning System for RGB to Point Cloud Translation using End2End Generative Network

Ali Ghofrani

Rahil Mahdian

Seyed Mojtaba Tabatabaie³

et al.

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2021

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APS: A Large-Scale Multi-modal Indoor Camera Positioning System

Ghofrani

Toroghi

Tabatabaie³

2021

Pattern Recognition and Artificial Intelligence

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Navigation inside a closed area with no GPS-signal accessibility is a highly challenging task. In order to tackle this problem, recently the imaging-based methods have grabbed the attention of many researchers. These methods either extract the features (e.g. using SIFT, or SOSNet) and map the descriptive ones to the camera position and rotation information, or deploy an end-to-end system that directly estimates this information out of RGB images, similar to PoseNet. While the former methods suffer from heavy computational burden during the test process, the latter suffers from lack of accuracy and robustness against environmental changes and object movements. However, end-to-end systems are quite fast during the test and inference and are pretty qualified for real-world applications, even though their training phase could be longer than the former ones. In this paper, a novel multi-modal end-to-end system for large-scale indoor positioning has been proposed, namely APS (Alpha Positioning System), which integrates a Pix2Pix GAN network to reconstruct the point cloud pair of the input query image, with a deep CNN network in order to robustly estimate the position and rotation information of the camera. For this integration, the existing datasets have the shortcoming of paired RGB/point cloud images for indoor environments. Therefore, we created a new dataset to handle this situation. By implementing the proposed APS system, we could achieve a highly accurate camera positioning with a precision level of less than a centimeter.

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APS: A Large-Scale Multi-modal Indoor Camera Positioning System

Ghofrani

Toroghi

Tabatabaie³

2021

Pattern Recognition and Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

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L-ICPSnet: LiDAR Indoor Camera Positioning System for RGB to Point Cloud Translation using End2End Generative Network

Cited by 1 publication

References 17 publications

APS: A Large-Scale Multi-modal Indoor Camera Positioning System

APS: A Large-Scale Multi-modal Indoor Camera Positioning System

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