Deep Localization of Static Scans in Mobile Mapping Point Clouds
| العنوان: | Deep Localization of Static Scans in Mobile Mapping Point Clouds |
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| المؤلفون: | Linh Truong-Hong, Bijun Li, Roderik Lindenbergh, Fancong Meng, Yufu Zang |
| المصدر: | Remote Sensing, 13(2) Remote Sensing Volume 13 Issue 2 Remote Sensing, Vol 13, Iss 219, p 219 (2021) |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Terrestrial laser scanning, Laser scanning, Computer science, Feature extraction, 0211 other engineering and technologies, Point cloud, 02 engineering and technology, RANSAC, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, lcsh:Science, Pose, 021101 geological & geomatics engineering, Block (data storage), business.industry, Mobile laser scanning, Iterative closest point, Place recogni-tion, place recognition, Point cloud localization, Pose refinement, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, lcsh:Q, Artificial intelligence, business, Mobile mapping |
| الوصف: | Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method). |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2072-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::938a15e10bbd9a8568ddccd609703bbb http://resolver.tudelft.nl/uuid:6628a046-d603-42bf-a2f4-d20611cd72fb |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....938a15e10bbd9a8568ddccd609703bbb |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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