Title
Deep Localization of Static Scans in Mobile Mapping Point Clouds
Author
Zang, Y. (TU Delft Optical and Laser Remote Sensing; Nanjing University of Information Sciences and Technology)
Meng, Fancong (Student TU Delft)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)
Truong-Hong, Linh (TU Delft Optical and Laser Remote Sensing)
Li, Bijun (Wuhan University)
Date
2021
Abstract
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).
Subject
Mobile laser scanning
Place recogni-tion
Point cloud localization
Pose refinement
Terrestrial laser scanning
To reference this document use:
http://resolver.tudelft.nl/uuid:6628a046-d603-42bf-a2f4-d20611cd72fb
DOI
https://doi.org/10.3390/rs13020219
ISSN
2072-4292
Source
Remote Sensing, 13 (2), 1-26
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2021 Y. Zang, Fancong Meng, R.C. Lindenbergh, Linh Truong-Hong, Bijun Li