scholarly journals Real-Time HD Map Change Detection for Crowdsourcing Update Based on Mid-to-High-End Sensors

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2477
Author(s):  
Pan Zhang ◽  
Mingming Zhang ◽  
Jingnan Liu
Keyword(s):  
Change Detection ◽  
Real Time ◽  
Point Cloud ◽  
Measurement Unit ◽  
Point Cloud Data ◽  
Integrated Navigation ◽  
High Definition ◽  
Cloud Data ◽  
Computing Platform ◽  
Semantic Point

Continuous maintenance and real-time update of high-definition (HD) maps is a big challenge. With the development of autonomous driving, more and more vehicles are equipped with a variety of advanced sensors and a powerful computing platform. Based on mid-to-high-end sensors including an industry camera, a high-end Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU), and an onboard computing platform, a real-time HD map change detection method for crowdsourcing update is proposed in this paper. First, a mature commercial integrated navigation product is directly used to achieve a self-positioning accuracy of 20 cm on average. Second, an improved network based on BiSeNet is utilized for real-time semantic segmentation. It achieves the result of 83.9% IOU (Intersection over Union) on Nvidia Pegasus at 31 FPS. Third, a visual Simultaneous Localization and Mapping (SLAM) associated with pixel type information is performed to obtain the semantic point cloud data of features such as lane dividers, road markings, and other static objects. Finally, the semantic point cloud data is vectorized after denoising and clustering, and the results are matched with a pre-constructed HD map to confirm map elements that have not changed and generate new elements when appearing. The experiment conducted in Beijing shows that the method proposed is effective for crowdsourcing update of HD maps.

scholarly journals A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

2020 ◽  
Vol 9 (2) ◽  
pp. 72 ◽  
Author(s):  
Sami El-Mahgary ◽  
Juho-Pekka Virtanen ◽  
Hannu Hyyppä
Keyword(s):  
Data Storage ◽  
Point Cloud ◽  
Point Clouds ◽  
Database Management System ◽  
Programming Approach ◽  
Point Cloud Data ◽  
Data Layout ◽  
Cloud Data ◽  
Semantic Point ◽  
Relational Database Management

The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

scholarly journals BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS

2015 ◽  
Vol II-3/W5 ◽  
pp. 393-400 ◽  
Author(s):  
M. Awrangjeb ◽  
C. S. Fraser ◽  
G. Lu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Component Analysis ◽  
Detection Technique ◽  
Building Detection ◽  
Point Cloud Data ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Cloud Data ◽  
Detection Techniques

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

Change detection in drill and blast tunnels from point cloud data

2018 ◽  
Vol 105 ◽  
pp. 172-181 ◽  
Author(s):  
Gabriel Walton ◽  
Mark S. Diederichs ◽  
Klaus Weinhardt ◽  
Dani Delaloye ◽  
Matthew J. Lato ◽  
...  
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Drill And Blast

scholarly journals Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Laser Scanner ◽  
Graph Cuts ◽  
Aerial Images ◽  
Processing Unit ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Surface Models

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

Creating a 3D Cuboid Map Using Multi-Layer 3D LIDAR with a Swing Mechanism

2018 ◽  
Vol 30 (4) ◽  
pp. 523-531 ◽  
Author(s):  
Yoshihiro Takita ◽  
Keyword(s):  
Coordinate Transformation ◽  
Point Cloud ◽  
Autonomous Robots ◽  
Point Cloud Data ◽  
High Definition ◽  
Cloud Data ◽  
Occupancy Grid ◽  
Occupancy Grid Maps ◽  
3D Lidar ◽  
Local Map

This paper proposes a method for creating 3D occupancy grid maps using multi-layer 3D LIDAR and a swing mechanism termed Swing-LIDAR. The method using Swing-LIDAR can acquire 10 times more data at a stopping position than a method that does not use Swing-LIDAR. High-definition and accurate terrain information is obtained by a coordinate transformation of the acquired data compensated for by the measured orientation of the system. In this study, we develop a method to create 3D grid maps for autonomous robots using Swing-LIDAR. To validate the method, AR Skipper is run on the created maps that are used to obtain point cloud data without a swing mechanism, and 11 sets of each local map are combined. The experimental results exhibit the differences among the maps.

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