scholarly journals Lidar Scan Registration Robust to Extreme Motions

2021 ◽  
Author(s):  
Simon-Pierre Deschenes ◽  
Dominic Baril ◽  
Vladimir Kubelka ◽  
Philippe Giguere ◽  
Francois Pomerleau
Keyword(s):  
Scan Registration

scholarly journals Automated placement of stereotactic injections using a laser scan of the skull

2014 ◽  
Author(s):  
Margaret Henderson ◽  
Vadim Pinskiy ◽  
Alexander Tolpygo ◽  
Stephen Savoia ◽  
Pascal Grange ◽  
...  
Keyword(s):  
Individual Variation ◽  
Point Cloud ◽  
Software Tool ◽  
Reference Points ◽  
Common Method ◽  
Matching Algorithm ◽  
Scan Registration ◽  
Automated Placement

Stereotactic targeting is a commonly used technique for performing injections in the brains of mice and other animals. The most common method for targeting stereoscopic injections uses the skull indentations bregma and lambda as reference points and is limited in its precision by factors such as skull curvature and individual variation, as well as an incomplete correspondence between skull landmarks and brain locations. In this software tool, a 3D laser scan of the mouse skull is taken in vitro and registered onto a reference skull using a point cloud matching algorithm, and the parameters of the transformation are used to position a glass pipette to place tracer injections. The software was capable of registering sample skulls with less than 100 micron error, and was able to target an injection in a mouse with error of roughly 500 microns. These results indicate that using skull scan registration has the potential to be widely applicable in automating stereotactic targeting of tracer injections.

scholarly journals Composite clustering normal distribution transform algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091214
Author(s):  
Tian Liu ◽  
Jiongzhi Zheng ◽  
Zhenting Wang ◽  
Zhengdong Huang ◽  
Yongfu Chen
Keyword(s):  
Normal Distribution ◽  
Experimental Result ◽  
Geometric Feature ◽  
Matching Accuracy ◽  
Localization And Mapping ◽  
Density Based Clustering ◽  
Scan Registration ◽  
Novel Method ◽  
Value Decomposition

Scan registration is a fundamental step for the simultaneous localization and mapping of mobile robot. The accuracy of scan registration is critical for the quality of mapping and the accuracy of robot navigation. During all of the scan registration methods, normal distribution transform is an efficient and wild-using one. But normal distribution transform will lead to the unreasonable interruption when splitting the grid and can’t express the points’ local geometric feature by prefixed grid. In this article, we propose a novel method, composite clustering normal distribution transform, which comprises the density-based clustering and k-means clustering to aggregate the points with similar local distributing feature. It takes singular value decomposition to judge the suitable degree of one cluster for further division. Meanwhile, to avoid the radiating phenomenon of LIDAR in measuring the points’ distance, we propose a method based on trigonometric to measure the internal distance. The clustering method in composite clustering normal distribution transform could ensure the expression of LIDAR’s local distribution and matching accuracy. The experimental result demonstrates that our method is more accurate and more stable than the normal distribution transform and iterative closest point methods.

A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

2019 ◽  
Vol 19 (24) ◽  
pp. 12333-12345
Author(s):  
Wenpeng Zong ◽  
Minglei Li ◽  
Yanglin Zhou ◽  
Li Wang ◽  
Fengzhuo Xiang ◽  
...  
Keyword(s):  
Registration Method ◽  
Scan Registration ◽  
Feature Based ◽  
Planar Feature

scholarly journals Range Scan Registration Using Reduced Deformable Models

2009 ◽  
Vol 28 (2) ◽  
pp. 447-456 ◽  
Author(s):  
W. Chang ◽  
M. Zwicker
Keyword(s):  
Deformable Models ◽  
Scan Registration

scholarly journals Enhancing Manual Scan Registration Using Audio Cues

2014 ◽  
Vol XL-4 ◽  
pp. 187-194
Author(s):  
T. Ntsoko ◽  
G. Sithole
Keyword(s):  
Point Cloud ◽  
Transfer Functions ◽  
Indoor Navigation ◽  
Cloud Data ◽  
Audio Cues ◽  
Area Of Interest ◽  
Points Of Interest ◽  
Other Information ◽  
Scan Registration ◽  
Manual Interaction

Indoor mapping and modelling requires that acquired data be processed by editing, fusing, formatting the data, amongst other operations. Currently the manual interaction the user has with the point cloud (data) while processing it is visual. Visual interaction does have limitations, however. One way of dealing with these limitations is to augment audio in point cloud processing. Audio augmentation entails associating points of interest in the point cloud with audio objects. In coarse scan registration, reverberation, intensity and frequency audio cues were exploited to help the user estimate depth and occupancy of space of points of interest. Depth estimations were made reliably well when intensity and frequency were both used as depth cues. Coarse changes of depth could be estimated in this manner. The depth between surfaces can therefore be estimated with the aid of the audio objects. Sound reflections of an audio object provided reliable information of the object surroundings in some instances. For a point/area of interest in the point cloud, these reflections can be used to determine the unseen events around that point/area of interest. Other processing techniques could benefit from this while other information is estimated using other audio cues like binaural cues and Head Related Transfer Functions. These other cues could be used in position estimations of audio objects to aid in problems such as indoor navigation problems.

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