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This commit is contained in:
Jan Kowalczyk
@@ -1164,6 +1164,53 @@
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\field{title}{1D MEMS Micro-Scanning LiDAR}
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\field{year}{2018}
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\endentry
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\entry{bg_slam}{article}{}{}
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\name{author}{2}{}{%
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{{hash=f20739d463254c239085b0098114da44}{%
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family={Smith},
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familyi={S\bibinitperiod},
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given={Randall\bibnamedelima C.},
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giveni={R\bibinitperiod\bibinitdelim C\bibinitperiod}}}%
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{{hash=9ec288d3d1be96333e0fae9796707e68}{%
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family={Cheeseman},
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familyi={C\bibinitperiod},
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given={Peter},
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giveni={P\bibinitperiod}}}%
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}
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\list{publisher}{1}{%
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{SAGE Publications}%
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}
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\strng{namehash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{fullhash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{fullhashraw}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{bibnamehash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{authorbibnamehash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{authornamehash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\strng{authorfullhash}{7031c0ebfd4f9d2d33ef0ddcb231c367}
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\field{labelnamesource}{author}
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\field{labeltitlesource}{title}
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\field{issn}{1741-3176}
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\field{journaltitle}{The International Journal of Robotics Research}
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\field{month}{12}
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\field{number}{4}
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\field{title}{On the Representation and Estimation of Spatial Uncertainty}
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\field{volume}{5}
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\field{year}{1986}
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\field{pages}{56\bibrangedash 68}
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\range{pages}{13}
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\verb{doi}
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\verb 10.1177/027836498600500404
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\endverb
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\verb{urlraw}
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\verb http://dx.doi.org/10.1177/027836498600500404
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\endverb
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\verb{url}
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\verb http://dx.doi.org/10.1177/027836498600500404
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\endverb
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\endentry
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\entry{lidar_denoising_survey}{article}{}{}
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\name{author}{4}{}{%
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{{hash=30663aad72dc59a49b7023f9c332b58a}{%
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@@ -1413,8 +1460,8 @@
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\strng{authornamehash}{d17e6557c5836d2d978179999ea1037f}
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\field{note}{\url{http://www.deeplearningbook.org}}
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@@ -1452,8 +1499,8 @@
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\field{month}{05}
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@@ -1494,8 +1541,8 @@
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\strng{authornamehash}{01a32420f9995c8592740c3ad622e775}
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\strng{authorfullhash}{c0310d5b84b91b546714624d9baf92c2}
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\field{issn}{1424-8220}
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@@ -1687,8 +1734,8 @@
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\strng{authornamehash}{1eed07a9c59db157d86a149850002efb}
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\field{issn}{1941-0468}
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@@ -1753,8 +1800,8 @@
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\strng{authorfullhash}{31c8cde264eb0da1d45f468f719f7a54}
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\strng{authorfullhashraw}{31c8cde264eb0da1d45f468f719f7a54}
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\field{extraname}{2}
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\field{labeltitlesource}{title}
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\field{booktitle}{2023 31st Mediterranean Conference on Control and Automation (MED)}
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@@ -1802,8 +1849,8 @@
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\strng{authornamehash}{ea684bebf6033a20ad34a33644ec89fc}
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\field{issn}{1556-4967}
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@@ -1851,8 +1898,8 @@
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\strng{authornamehash}{5e0b9f9cab8ce61be5266767752c12dc}
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\field{abstract}{Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed.}
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@@ -1893,8 +1940,8 @@
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\field{labelnamesource}{author}
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\field{labeltitlesource}{title}
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\field{title}{ODDS Library}
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@@ -1941,8 +1988,8 @@
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\strng{authornamehash}{dd2ddc978fe083bcff1aa1379cd19643}
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\field{issn}{0018-9219}
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@@ -2017,8 +2064,8 @@
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\strng{authornamehash}{0fca66725a9966a967fc7893b180ddef}
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\field{issn}{2296-7745}
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@@ -2091,8 +2138,8 @@
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\strng{authornamehash}{e1fc6cab9b6009340e110518e53868c4}
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\field{labeltitlesource}{title}
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\field{title}{MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications}
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@@ -2143,8 +2190,8 @@
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\strng{authorfullhash}{2b7b29fe45fee2bd5ddb1dd1cbbff521}
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\field{labeltitlesource}{title}
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\field{booktitle}{2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}
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