東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches./
作者:	Flanagan, Lauren E.
面頁冊數:	1 online resource (118 pages)
附註:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
標題:	Aeronautics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29239037click for full text (PQDT)
ISBN:	9798438767091

Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches.
Flanagan, Lauren E.

Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches. - 1 online resource (118 pages)

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.Eng.Sc.)--The University of Western Ontario (Canada), 2022.

Includes bibliographical references

Rover navigation on planetary surfaces currently uses a method called blind drive which requires a navigation goal as input from operators on Earth and uses camera images to autonomously detect obstacles. Images can be affected by lighting conditions, are not highly accurate from far distances, and will not work in the dark; these factors negatively impact the autonomous capabilities of rovers. By improving a rover's ability to autonomously detect obstacles, the capabilities of rovers in future missions would improve; for example, enabling exploration of permanently shadowed regions, and allowing faster driving speeds and farther travel distances. This thesis demonstrates how Lidar point clouds can be used to autonomously and efficiently segment planetary terrain to identify obstacles for safe rover navigation. Two Lidar datasets which represent planetary environments containing rock obstacles and sandy terrain were used to train a neural network to perform semantic segmentation. The neural network was based on the RandLA-Net architecture that was designed to efficiently perform semantic segmentation on point clouds using a random sampling algorithm without modifying the point cloud structure. Methods to handle the class imbalance of the datasets were explored to enable the model to learn the minority class and to optimize the model's performance. The model achieved a recall score of 94.46% and precision score of 84.93% at a frame rate of 0.6238 seconds/point cloud on an Intel Xeon E5-2665 CPU, indicating that it is possible to use Lidar point clouds to perform semantic segmentation on-board planetary rovers with similar compute capabilities.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798438767091Subjects--Topical Terms:

560293
Aeronautics.
Index Terms--Genre/Form:

542853
Electronic books.

Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches.
LDR:02958nmm a2200361K 4500 001 2361245
005 20231023072124.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798438767091
035 $a (MiAaPQ)AAI29239037
035 $a (MiAaPQ)oaiirlibuwocaetd11044
035 $a AAI29239037
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Flanagan, Lauren E. $3 3701901
245 1 0 $a Autonomous Rock Segmentation from Lidar Point Clouds Using Machine Learning Approaches.
264 0 $c 2022
300 $a 1 online resource (118 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 83-11.
500 $a Advisor: McIsaac, Kenneth.
502 $a Thesis (M.Eng.Sc.)--The University of Western Ontario (Canada), 2022.
504 $a Includes bibliographical references
520 $a Rover navigation on planetary surfaces currently uses a method called blind drive which requires a navigation goal as input from operators on Earth and uses camera images to autonomously detect obstacles. Images can be affected by lighting conditions, are not highly accurate from far distances, and will not work in the dark; these factors negatively impact the autonomous capabilities of rovers. By improving a rover's ability to autonomously detect obstacles, the capabilities of rovers in future missions would improve; for example, enabling exploration of permanently shadowed regions, and allowing faster driving speeds and farther travel distances. This thesis demonstrates how Lidar point clouds can be used to autonomously and efficiently segment planetary terrain to identify obstacles for safe rover navigation. Two Lidar datasets which represent planetary environments containing rock obstacles and sandy terrain were used to train a neural network to perform semantic segmentation. The neural network was based on the RandLA-Net architecture that was designed to efficiently perform semantic segmentation on point clouds using a random sampling algorithm without modifying the point cloud structure. Methods to handle the class imbalance of the datasets were explored to enable the model to learn the minority class and to optimize the model's performance. The model achieved a recall score of 94.46% and precision score of 84.93% at a frame rate of 0.6238 seconds/point cloud on an Intel Xeon E5-2665 CPU, indicating that it is possible to use Lidar point clouds to perform semantic segmentation on-board planetary rovers with similar compute capabilities.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Aeronautics. $3 560293
650 4 $a Cameras. $3 524039
650 4 $a Lighting. $3 808651
650 4 $a Moon. $3 539144
650 4 $a Neural networks. $3 677449
650 4 $a Mars. $3 3687764
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Electrical engineering. $3 649834
650 4 $a Robotics. $3 519753
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0800
690 $a 0771
690 $a 0544
690 $a 0538
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Western Ontario (Canada). $3 1017622
773 0 $t Masters Abstracts International $g 83-11.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29239037 $z click for full text (PQDT)