scholarly journals Robust Scene Stitching in Large Scale Mobile Mapping

2013 ◽  
Author(s):  
Filip Schouwenaars ◽  
Radu Timofte ◽  
Luc Van Gool
Keyword(s):  
Large Scale ◽  
Mobile Mapping

scholarly journals CENTIMETRE-ACCURACY IN FORESTS AND URBAN CANYONS – COMBINING A HIGH-PERFORMANCE IMAGE-BASED MOBILE MAPPING BACKPACK WITH NEW GEOREFERENCING METHODS

2020 ◽  
Vol V-1-2020 ◽  
pp. 333-341
Author(s):  
S. Blaser ◽  
J. Meyer ◽  
S. Nebiker ◽  
L. Fricker ◽  
D. Weber
Keyword(s):  
Web Services ◽  
High Performance ◽  
Large Scale ◽  
Superior Performance ◽  
City Centre ◽  
Visual Localization ◽  
Mobile Mapping ◽  
Coordinate Measurement ◽  
Collaborative Processes ◽  
Mobile Mapping System

Abstract. Advances in digitalization technologies lead to rapid and massive changes in infrastructure management. New collaborative processes and workflows require detailed, accurate and up-to-date 3D geodata. Image-based web services with 3D measurement functionality, for example, transfer dangerous and costly inspection and measurement tasks from the field to the office workplace. In this contribution, we introduced an image-based backpack mobile mapping system and new georeferencing methods for capture previously inaccessible outdoor locations. We carried out large-scale performance investigations at two different test sites located in a city centre and in a forest area. We compared the performance of direct, SLAM-based and image-based georeferencing under demanding real-world conditions. Both test sites include areas with restricted GNSS reception, poor illumination, and uniform or ambiguous geometry, which create major challenges for reliable and accurate georeferencing. In our comparison of georeferencing methods, image-based georeferencing improved the median precision of coordinate measurement over direct georeferencing by a factor of 10–15 to 3 mm. Image-based georeferencing also showed a superior performance in terms of absolute accuracies with results in the range from 4.3 cm to 13.2 cm. Our investigations showed a great potential for complementing 3D image-based geospatial web-services of cities as well as for creating such web services for forest applications. In addition, such accurately georeferenced 3D imagery has an enormous potential for future visual localization and augmented reality applications.

scholarly journals A LASER-SLAM ALGORITHM FOR INDOOR MOBILE MAPPING

2016 ◽  
Vol XLI-B4 ◽  
pp. 351-355
Author(s):  
Wenjun Zhang ◽  
Qiao Zhang ◽  
Kai Sun ◽  
Sheng Guo
Keyword(s):  
Indoor Environment ◽  
Large Scale ◽  
Real Data ◽  
Tree Structure ◽  
Experimental Results ◽  
Laser Sensor ◽  
Mobile Mapping ◽  
Shopping Centres ◽  
Graph Optimization ◽  
Slam Algorithm

A novel Laser-SLAM algorithm is presented for real indoor environment mobile mapping. SLAM algorithm can be divided into two classes, Bayes filter-based and graph optimization-based. The former is often difficult to guarantee consistency and accuracy in largescale environment mapping because of the accumulative error during incremental mapping. Graph optimization-based SLAM method often assume predetermined landmarks, which is difficult to be got in unknown environment mapping. And there most likely has large difference between the optimize result and the real data, because the constraints are too few. This paper designed a kind of sub-map method, which could map more accurately without predetermined landmarks and avoid the already-drawn map impact on agent’s location. The tree structure of sub-map can be indexed quickly and reduce the amount of memory consuming when mapping. The algorithm combined Bayes-based and graph optimization-based SLAM algorithm. It created virtual landmarks automatically by associating data of sub-maps for graph optimization. Then graph optimization guaranteed consistency and accuracy in large-scale environment mapping and improved the reasonability and reliability of the optimize results. Experimental results are presented with a laser sensor (UTM 30LX) in official buildings and shopping centres, which prove that the proposed algorithm can obtain 2D maps within 10cm precision in indoor environment range from several hundreds to 12000 square meter.

scholarly journals A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis

2020 ◽  
Vol 10 (7) ◽  
pp. 2420 ◽  
Author(s):  
Jian Tang ◽  
Jingren Wen ◽  
Chuang Qian
Keyword(s):  
Large Scale ◽  
Small Region ◽  
Mapping Method ◽  
Indoor Navigation ◽  
Relative Accuracy ◽  
Control Network ◽  
Absolute Accuracy ◽  
Mobile Mapping ◽  
Loop Closure ◽  
Localization And Mapping

Indoor mobile mapping techniques are important for indoor navigation and indoor modeling. As an efficient method, Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) has been applied for fast indoor mobile mapping. It can quickly construct high-precision indoor maps in a certain small region. However, with the expansion of the mapping area, SLAM-based mapping methods face many difficulties, such as loop closure detection, large amounts of calculation, large memory occupation, and limited mapping precision. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM to solve the problem of mapping for large-scale environments. Its effectiveness is analyzed from the relative accuracy and absolute accuracy of the mapping results. The experimental results show that the relative accuracy can reach 0.08 m, an improvement of 49.8% compared to the mapping result without loop closure. The absolute accuracy can reach 0.13 m, which proves the method’s feasibility for distributed mapping. The accuracies under different numbers of control points are also compared to find the suitable structure of the control network.

scholarly journals Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

2014 ◽  
Vol XL-3 ◽  
pp. 73-80
Author(s):  
D. Craciun ◽  
A. Serna Morales ◽  
J.-E. Deschaud ◽  
B. Marcotegui ◽  
F. Goulette
Keyword(s):  
Surface Reconstruction ◽  
Large Scale ◽  
Ground Surface ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Mobile Mapping ◽  
3D Point Clouds ◽  
Mesh Shrinkage ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hyungki Kim ◽  
Yuna Kang ◽  
Soonhung Han
Keyword(s):  
Virtual Reality ◽  
Visual Information ◽  
Large Scale ◽  
Mobile Mapping ◽  
3D Navigation ◽  
Digital Map ◽  
Mapping System ◽  
City Modeling ◽  
Mobile Mapping System ◽  
City Models

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model.

scholarly journals Deep Localization of Static Scans in Mobile Mapping Point Clouds

2021 ◽  
Vol 13 (2) ◽  
pp. 219
Author(s):  
Yufu Zang ◽  
Fancong Meng ◽  
Roderik Lindenbergh ◽  
Linh Truong-Hong ◽  
Bijun Li
Keyword(s):  
Neural Network ◽  
Laser Scanning ◽  
Large Scale ◽  
Reference Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Place Recognition ◽  
Global Features ◽  
Mobile Mapping ◽  
Mapping Point

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).

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