scholarly journals A Survey of Low-Cost 3D Laser Scanning Technology

2021 ◽  
Vol 11 (9) ◽  
pp. 3938
Author(s):  
Shusheng Bi ◽  
Chang Yuan ◽  
Chang Liu ◽  
Jun Cheng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Moving Objects ◽  
Low Cost ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Future Research ◽  
3D Laser Scanning ◽  
Time Performance ◽  
Improving Accuracy ◽  
3D Lidar

By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.

scholarly journals Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3837 ◽  
Author(s):  
Junjie Zeng ◽  
Rusheng Ju ◽  
Long Qin ◽  
Yue Hu ◽  
Quanjun Yin ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Domain Knowledge ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Continuous Control ◽  
Complex Environments ◽  
Reward Function ◽  
Knowledge Based ◽  
Task Architecture

In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot’s capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration commands.

scholarly journals Semi-automatic Low Cost 3D Laser Scanning Systems for Reverse Engineering

Procedia CIRP ◽  
2015 ◽  
Vol 28 ◽  
pp. 94-99 ◽  
Author(s):  
L.M. Galantucci ◽  
E. Piperi ◽  
F. Lavecchia ◽  
A. Zhavo
Keyword(s):  
Reverse Engineering ◽  
Laser Scanning ◽  
Low Cost ◽  
3D Laser Scanning ◽  
Scanning Systems

scholarly journals Forensic Engineering Application of the Matchmoving Process

2018 ◽  
Vol 35 (2) ◽  
Author(s):  
Richard M. Ziernicki ◽  
Angelos G. Leiloglo ◽  
Taylor Spiegelberg ◽  
Kurt Twigg
Keyword(s):  
Laser Scanning ◽  
Moving Objects ◽  
Engineering Application ◽  
3D Laser Scanning ◽  
Scientific Process ◽  
High Definition ◽  
Virtual Camera ◽  
Video Footage ◽  
Forensic Engineering ◽  
Race Track

This paper presents a methodology that uses the photogrammetric process of matchmoving for analyzing objects (vehicles, pedestrians, etc.) visible in video captured by moving cameras. Matchmoving is an established scientific process that is used to calibrate a virtual camera to “match” the movement and optic properties of the real-world camera that captured the video. High-definition 3D laser scanning technology makes it possible to accurately perform the matchmoving process and evaluate the results. Once a virtual camera is accurately calibrated, moving objects visible in the video can be tracked or matched to determine their position, orientation, path, speed, and acceleration. Specific applications of the matchmoving methodology are presented and discussed in this paper and include analysis performed on video footage from a metro bus on-board camera, police officer body-worn camera footage, and race track video footage captured by a drone. In all cases, the matchmoving process yielded highly accurate camera calibrations and allowed forensic investigators to accurately determine and evaluate the dynamics of moving objects depicted in the video.

Low-cost Electrothermally Actuated MEMS Mirrors for High-Speed 3D Laser Scanning Applications

2021 ◽  
Author(s):  
Bibek R. Samanta ◽  
Flavio Pardo ◽  
Rose Kopf ◽  
Michael S. Eggleston
Keyword(s):  
High Speed ◽  
Laser Scanning ◽  
Low Cost ◽  
3D Laser Scanning

scholarly journals SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 295-302 ◽  
Author(s):  
Wen Xiao ◽  
Bruno Vallet ◽  
Konrad Schindler ◽  
Nicolas Paparoditis
Keyword(s):  
Energy Function ◽  
Laser Scanning ◽  
Moving Objects ◽  
Pedestrian Detection ◽  
Simultaneous Detection ◽  
Dynamic Environment ◽  
Space Time ◽  
Detection And Tracking ◽  
Nearest Point ◽  
Pedestrian Traffic

Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, <i>Nearest-point</i> and <i>Max-distance</i>. Then, all the points on moving objects are transferred into a space-time (<i>x</i>, <i>y</i>, <i>t</i>) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.

scholarly journals SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 295-302
Author(s):  
Wen Xiao ◽  
Bruno Vallet ◽  
Konrad Schindler ◽  
Nicolas Paparoditis
Keyword(s):  
Energy Function ◽  
Laser Scanning ◽  
Moving Objects ◽  
Pedestrian Detection ◽  
Simultaneous Detection ◽  
Dynamic Environment ◽  
Space Time ◽  
Detection And Tracking ◽  
Nearest Point ◽  
Pedestrian Traffic

Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, <i>Nearest-point</i> and <i>Max-distance</i>. Then, all the points on moving objects are transferred into a space-time (<i>x</i>, <i>y</i>, <i>t</i>) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Yihuan Zhang ◽  
Liang Wang ◽  
Xuhui Jiang ◽  
Yong Zeng ◽  
Yifan Dai
Keyword(s):  
Low Cost ◽  
Dynamic Environments ◽  
Map Matching ◽  
Positioning Systems ◽  
Localization Method ◽  
Segmentation Methods ◽  
Global Positioning ◽  
Iterative Closest Point Algorithm ◽  
Self Driving Cars ◽  
3D Lidar

Abstract Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Measurement of erosion state and refractory lining thickness of blast furnace hearth by using three-dimensional laser scanning method

2020 ◽  
Vol 118 (1) ◽  
pp. 106
Author(s):  
Lei Zhang ◽  
Jianliang Zhang ◽  
Kexin Jiao ◽  
Guoli Jia ◽  
Jian Gong ◽  
...  
Keyword(s):  
Blast Furnace ◽  
Laser Scanning ◽  
Refractory Lining ◽  
Three Dimensional ◽  
3D Laser Scanning ◽  
Scanning Method ◽  
Blast Furnace Production ◽  
Severe Erosion ◽  
Residual Thickness ◽  
Minimum Depth

The three-dimensional (3D) model of erosion state of blast furnace (BF) hearth was obtained by using 3D laser scanning method. The thickness of refractory lining can be measured anywhere and the erosion curves were extracted both in the circumferential and height directions to analyze the erosion characteristics. The results show that the most eroded positions located below 20# tuyere with an elevation of 7700 mm and below 24#–25# tuyere with an elevation of 8100 mm, the residual thickness here is only 295 mm. In the circumferential directions, the serious eroded areas located between every two tapholes while the taphole areas were protected well by the bonding material. In the height directions, the severe erosion areas located between the elevation of 7600 mm to 8200 mm. According to the calculation, the minimum depth to ensure the deadman floats in the hearth is 2581 mm, corresponding to the elevation of 7619 mm. It can be considered that during the blast furnace production process, the deadman has been sinking to the bottom of BF hearth and the erosion areas gradually formed at the root of deadman.

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