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DeepMapping2.5 : = Lightweight LiDAR Map Optimization.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
DeepMapping2.5 :/
其他題名:
Lightweight LiDAR Map Optimization.
作者:
Chen, Xianhui.
面頁冊數:
1 online resource (56 pages)
附註:
Source: Masters Abstracts International, Volume: 85-08.
Contained By:
Masters Abstracts International85-08.
標題:
Computer engineering. -
電子資源:
click for full text (PQDT)
ISBN:
9798381695304
DeepMapping2.5 : = Lightweight LiDAR Map Optimization.
Chen, Xianhui.
DeepMapping2.5 :
Lightweight LiDAR Map Optimization. - 1 online resource (56 pages)
Source: Masters Abstracts International, Volume: 85-08.
Thesis (M.S.)--New York University Tandon School of Engineering, 2024.
Includes bibliographical references
Lidar mapping plays a crucial role in self-driving and mobile robotics. DeepMapping2 is a large-scaled LiDAR global optimization method. However, it increases parameter usage, leading to time-consuming computations. To tackle this issue, we propose DeepMapping2.5, which enhances generalization capability and resolves the problem of heavyweight parameters. The key innovations of DeepMapping2.5 involve stabilizing rotation representation using quaternions and reducing training time through knowledge distillation from DeepMapping2. Experimental results on the KITTI dataset demonstrate that DeepMapping2.5 achieves competitive performance compared to state-of-the-art LiDAR mapping methods, all while utilizing fewer training parameters and shorter training time.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798381695304Subjects--Topical Terms:
569006
Computer engineering.
Subjects--Index Terms:
DeepMapping2Index Terms--Genre/Form:
554714
Electronic books.
DeepMapping2.5 : = Lightweight LiDAR Map Optimization.
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Lightweight LiDAR Map Optimization.
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Source: Masters Abstracts International, Volume: 85-08.
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Includes bibliographical references
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Lidar mapping plays a crucial role in self-driving and mobile robotics. DeepMapping2 is a large-scaled LiDAR global optimization method. However, it increases parameter usage, leading to time-consuming computations. To tackle this issue, we propose DeepMapping2.5, which enhances generalization capability and resolves the problem of heavyweight parameters. The key innovations of DeepMapping2.5 involve stabilizing rotation representation using quaternions and reducing training time through knowledge distillation from DeepMapping2. Experimental results on the KITTI dataset demonstrate that DeepMapping2.5 achieves competitive performance compared to state-of-the-art LiDAR mapping methods, all while utilizing fewer training parameters and shorter training time.
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click for full text (PQDT)
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