scholarly journals Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5340
Author(s):  
Carlos Prados Sesmero ◽  
Sergio Villanueva Lorente ◽  
Mario Di Castro
Keyword(s):  
Point Clouds ◽  
Initial Position ◽  
Robot Localization ◽  
Environmental Reconstruction ◽  
Original Algorithm ◽  
Robot Trajectory ◽  
Constraint Graph ◽  
Multiple Environments ◽  
Graph Slam

This paper presents a fully original algorithm of graph SLAM developed for multiple environments—in particular, for tunnel applications where the paucity of features and the difficult distinction between different positions in the environment is a problem to be solved. This algorithm is modular, generic, and expandable to all types of sensors based on point clouds generation. The algorithm may be used for environmental reconstruction to generate precise models of the surroundings. The structure of the algorithm includes three main modules. One module estimates the initial position of the sensor or the robot, while another improves the previous estimation using point clouds. The last module generates an over-constraint graph that includes the point clouds, the sensor or the robot trajectory, as well as the relation between positions in the trajectory and the loop closures.

scholarly journals Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3928 ◽  
Author(s):  
Weisong Wen ◽  
Li-Ta Hsu ◽  
Guohao Zhang
Keyword(s):  
New York ◽  
Hong Kong ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Tall Buildings ◽  
Point Clouds ◽  
Reliability Estimation ◽  
Traffic Condition ◽  
Localization And Mapping ◽  
Graph Slam

Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.

scholarly journals A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Rendong Wang ◽  
Youchun Xu ◽  
Miguel Angel Sotelo ◽  
Yulin Ma ◽  
Thompson Sarkodie-Gyan ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Initial Position ◽  
Urban Environments ◽  
Data Set ◽  
Registration Method ◽  
Position Errors ◽  
Initial Errors ◽  
High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

scholarly journals Trajectory Planning of a 6D Robot based on Meta Heuristic Algorithms

2018 ◽  
Vol 220 ◽  
pp. 06004 ◽  
Author(s):  
Kritsada Wichapong ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Thana Radpukdee
Keyword(s):  
Trajectory Planning ◽  
Heuristic Algorithms ◽  
Initial Position ◽  
Intermediate Position ◽  
Motion Path ◽  
Robot Trajectory ◽  
Design Variables ◽  
Travelling Time ◽  
Fourth Order Polynomial ◽  
Robot Trajectory Planning

In this work, several established meta-heuristics (MHs) were employed for solving 6-DOF robot trajectory planning. A fourth order polynomial function is used to represent a motion path of the robot from initial to final points while an optimisation problem is posed to minimise travelling time subject to velocity, acceleration and jerk constraints. The design variables are joint velocities and accelerations at intermediate positions, and moving time from the initial position to the intermediate position and from the intermediate position to the final position. Several MHs are used to solve the trajectory optimisation problem of robot manipulators while their performances are investigated. Based on this study, the best MH for robot trajectory planning is found while the results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation.

scholarly journals ELiT, Multifunctional Web-Software for Feature Extraction from 3D LiDAR Point Clouds

2020 ◽  
Vol 9 (11) ◽  
pp. 650
Author(s):  
Sergiy Kostrikov ◽  
Rostyslav Pudlo ◽  
Dmytro Bubnov ◽  
Vladimir Vasiliev
Keyword(s):  
Feature Extraction ◽  
Point Clouds ◽  
Use Cases ◽  
Original Algorithm ◽  
Building Models ◽  
Server Software ◽  
Urban Terrain ◽  
Geospatial Services ◽  
3D Lidar ◽  
Terrain Generation

Our research presents a complete R&D cycle—from the urban terrain generation and feature extraction by raw LiDAR data processing, through visualizing a huge number of urban features, and till applied thematic use cases based on these features extracted and modeled. Firstly, the paper focuses on the original contribution to algorithmic solutions concerning the fully automated extraction of building models with the urban terrain generation. Topography modeling and extraction of buildings, as two key constituents of the robust algorithmic pipeline, have been examined. The architectural scheme of the multifunctional software family—EOS LIDAR Tool (ELiT) has been presented with characteristics of its key functionalities and examples of a user interface. Both desktop, and web server software, as well as a cloud-based application, ELiT Geoportal (EGP), as an entity for online geospatial services, have been elaborated on the base of the approach presented. Further emphasis on the web-visualization with Cesium 3D Tiles has demonstrated the original algorithm for efficient feature visualizing though the EGP locations. Summarizing presentation of two thematic use-cases has finalized this research, demonstrating those applied tasks, which can be efficiently resolved with the workflow presented. A necessity of a conclusive workflow elaboration for use cases, which would be based on the actual semantics, has been emphasized.

scholarly journals Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

2021 ◽  
Vol 13 (24) ◽  
pp. 5066
Author(s):  
Mohammad Aldibaja ◽  
Naoki Suganuma
Keyword(s):  
Autonomous Vehicles ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Railway Bridges ◽  
The Road ◽  
Position Errors ◽  
Challenging Environment ◽  
Graph Slam ◽  
Unique Graph ◽  
Absolute Coordinate System

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.

scholarly journals OC-SLAM: Steadily Tracking and Mapping in Dynamic Environments

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhenyu Wu ◽  
Xiangyu Deng ◽  
Shengming Li ◽  
Yingshun Li
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Detection Algorithm ◽  
Dynamic Features ◽  
Dynamic Scenes ◽  
Original Algorithm ◽  
Localization And Mapping ◽  
Dense Point

Visual Simultaneous Localization and Mapping (SLAM) system is mainly used in real-time localization and mapping tasks of robots in various complex environments, while traditional monocular vision algorithms are struggling to cope with weak texture and dynamic scenes. To solve these problems, this work presents an object detection and clustering assisted SLAM algorithm (OC-SLAM), which adopts a faster object detection algorithm to add semantic information to the image and conducts geometrical constraint on the dynamic keypoints in the prediction box to optimize the camera pose. It also uses RGB-D camera to perform dense point cloud reconstruction with the dynamic objects rejected, and facilitates European clustering of dense point clouds to jointly eliminate dynamic features combining with object detection algorithm. Experiments in the TUM dataset indicate that OC-SLAM enhances the localization accuracy of the SLAM system in the dynamic environments compared with original algorithm and it has shown impressive performance in the localizition and can build a more precise dense point cloud map in dynamic scenes.

scholarly journals AN ORIGINAL ALGORITHM FOR BIM GENERATION FROM INDOOR SURVEY POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 769-776 ◽  
Author(s):  
F. Capocchiano ◽  
R. Ravanelli
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Point Clouds ◽  
Automatic Extraction ◽  
Original Algorithm ◽  
Preliminary Results ◽  
3D Shape ◽  
Laser Scanners ◽  
Geometrical Information ◽  
New Strategies

<p><strong>Abstract.</strong> Nowadays, it is essential to find new strategies, able to perform the first step of the scan-to-BIM process, by retrieving the geometrical information contained in point clouds that are so easily collected through laser scanners and range cameras. This paper presents a new algorithm for the automatic extraction of the layout and the height of a small indoor environment from its point cloud. In particular, the algorithm was tested on a point cloud of 600000 vertices, selected from the dataset of the ISPRS benchmark on indoor modelling. The preliminary results are encouraging: the 3D shape (layout and height) of the investigated room is effectively reconstructed.</p>

Quantitative analysis by reconstruction of HREM focal series

1994 ◽  
Vol 52 ◽  
pp. 740-741
Author(s):  
W. Coene ◽  
A. Thust ◽  
M. Op de Beeck ◽  
D. Van Dyck
Keyword(s):  
Phase Retrieval ◽  
Image Plane ◽  
Initial Guess ◽  
Recursive Least Squares ◽  
Objective Lens ◽  
Electron Wave ◽  
Electron Sources ◽  
Original Algorithm ◽  
Coherent Field ◽  
Direct Interpretation

Compared to conventional electron sources, the use of a highly coherent field-emission gun (FEG) in TEM improves the information resolution considerably. A direct interpretation of this extra information, however, is hampered since amplitude and phase of the electron wave are scrambled in a complicated way upon transfer from the specimen exit plane through the objective lens towards the image plane. In order to make the additional high-resolution information interpretable, a phase retrieval procedure is applied, which yields the aberration-corrected electron wave from a focal series of HRTEM images (Coene et al, 1992).Kirkland (1984) tackled non-linear image reconstruction using a recursive least-squares formalism in which the electron wave is modified stepwise towards the solution which optimally matches the contrast features in the experimental through-focus series. The original algorithm suffers from two major drawbacks : first, the result depends strongly on the quality of the initial guess of the first step, second, the processing time is impractically high.

The Multiple Environments in Which We Grow Old

1984 ◽  
Vol 29 (3) ◽  
pp. 230-231
Author(s):  
Frances M. Carp
Keyword(s):  
Multiple Environments
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