@article{anomaly_detection_survey,
  author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
  title = {Anomaly detection: A survey},
  year = {2009},
  issue_date = {July 2009},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {41},
  number = {3},
  issn = {0360-0300},
  url = {https://doi.org/10.1145/1541880.1541882},
  doi = {10.1145/1541880.1541882},
  abstract = {Anomaly detection is an important problem that has been researched
              within diverse research areas and application domains. Many anomaly
              detection techniques have been specifically developed for certain
              application domains, while others are more generic. This survey
              tries to provide a structured and comprehensive overview of the
              research on anomaly detection. We have grouped existing techniques
              into different categories based on the underlying approach adopted
              by each technique. For each category we have identified key
              assumptions, which are used by the techniques to differentiate
              between normal and anomalous behavior. When applying a given
              technique to a particular domain, these assumptions can be used as
              guidelines to assess the effectiveness of the technique in that
              domain. For each category, we provide a basic anomaly detection
              technique, and then show how the different existing techniques in
              that category are variants of the basic technique. This template
              provides an easier and more succinct understanding of the
              techniques belonging to each category. Further, for each category,
              we identify the advantages and disadvantages of the techniques in
              that category. We also provide a discussion on the computational
              complexity of the techniques since it is an important issue in real
              application domains. We hope that this survey will provide a better
              understanding of the different directions in which research has
              been done on this topic, and how techniques developed in one area
              can be applied in domains for which they were not intended to begin
              with.},
  journal = {ACM Comput. Surv.},
  month = jul,
  articleno = {15},
  numpages = {58},
  keywords = {outlier detection, Anomaly detection},
},
@dataset{alexander_kyuroson_2023_7913307,
  author = {Alexander Kyuroson and Niklas Dahlquist and Nikolaos Stathoulopoulos
            and Vignesh Kottayam Viswanathan and Anton Koval and George
            Nikolakopoulos},
  title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
           Particles for Frontier Exploration },
  month = may,
  year = 2023,
  publisher = {Zenodo},
  version = {v1},
  doi = {10.5281/zenodo.7913307},
  url = {https://doi.org/10.5281/zenodo.7913307},
},
@article{deepsad,
  author = {Lukas Ruff and Robert A. Vandermeulen and Nico G{\"{o}}rnitz and
            Alexander Binder and Emmanuel M{\"{u}}ller and Klaus{-}Robert M{\"{u}
            }ller and Marius Kloft},
  title = {Deep Semi-Supervised Anomaly Detection},
  journal = {CoRR},
  volume = {abs/1906.02694},
  year = {2019},
  url = {http://arxiv.org/abs/1906.02694},
  eprinttype = {arXiv},
  eprint = {1906.02694},
  timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
  biburl = {https://dblp.org/rec/journals/corr/abs-1906-02694.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
},
@inproceedings{subter,
  title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
           Particles for Frontier Exploration},
  url = {http://dx.doi.org/10.1109/MED59994.2023.10185906},
  DOI = {10.1109/med59994.2023.10185906},
  booktitle = {2023 31st Mediterranean Conference on Control and Automation
               (MED)},
  publisher = {IEEE},
  author = {Kyuroson, Alexander and Dahlquist, Niklas and Stathoulopoulos,
            Nikolaos and Viswanathan, Vignesh Kottayam and Koval, Anton and
            Nikolakopoulos, George},
  year = {2023},
  month = jun,
  pages = {716–721},
}
,
@inproceedings{deepsvdd,
  title = {Deep One-Class Classification},
  author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
            Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
            , Emmanuel and Kloft, Marius},
  booktitle = {Proceedings of the 35th International Conference on Machine
               Learning},
  pages = {4393--4402},
  year = {2018},
  editor = {Dy, Jennifer and Krause, Andreas},
  volume = {80},
  series = {Proceedings of Machine Learning Research},
  month = {10--15 Jul},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
  url = {https://proceedings.mlr.press/v80/ruff18a.html},
  abstract = {Despite the great advances made by deep learning in many machine
              learning problems, there is a relative dearth of deep learning
              approaches for anomaly detection. Those approaches which do exist
              involve networks trained to perform a task other than anomaly
              detection, namely generative models or compression, which are in
              turn adapted for use in anomaly detection; they are not trained on
              an anomaly detection based objective. In this paper we introduce a
              new anomaly detection method—Deep Support Vector Data Description—,
              which is trained on an anomaly detection based objective. The
              adaptation to the deep regime necessitates that our neural network
              and training procedure satisfy certain properties, which we
              demonstrate theoretically. We show the effectiveness of our method
              on MNIST and CIFAR-10 image benchmark datasets as well as on the
              detection of adversarial examples of GTSRB stop signs.},
},
@inproceedings{deep_svdd,
  title = {Deep One-Class Classification},
  author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
            Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
            , Emmanuel and Kloft, Marius},
  booktitle = {Proceedings of the 35th International Conference on Machine
               Learning},
  pages = {4393--4402},
  year = {2018},
  editor = {Dy, Jennifer and Krause, Andreas},
  volume = {80},
  series = {Proceedings of Machine Learning Research},
  month = {10--15 Jul},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
  url = {https://proceedings.mlr.press/v80/ruff18a.html},
  abstract = {Despite the great advances made by deep learning in many machine
              learning problems, there is a relative dearth of deep learning
              approaches for anomaly detection. Those approaches which do exist
              involve networks trained to perform a task other than anomaly
              detection, namely generative models or compression, which are in
              turn adapted for use in anomaly detection; they are not trained on
              an anomaly detection based objective. In this paper we introduce a
              new anomaly detection method—Deep Support Vector Data Description—,
              which is trained on an anomaly detection based objective. The
              adaptation to the deep regime necessitates that our neural network
              and training procedure satisfy certain properties, which we
              demonstrate theoretically. We show the effectiveness of our method
              on MNIST and CIFAR-10 image benchmark datasets as well as on the
              detection of adversarial examples of GTSRB stop signs.},
},
  @inproceedings{anomaly_detection_medical,
  author = {{Wei}, Qi and {Ren}, Yinhao and {Hou}, Rui and {Shi}, Bibo and {Lo},
            Joseph Y. and {Carin}, Lawrence},
  title = "{Anomaly detection for medical images based on a one-class
           classification}",
  booktitle = {Medical Imaging 2018: Computer-Aided Diagnosis},
  year = 2018,
  editor = {{Petrick}, Nicholas and {Mori}, Kensaku},
  series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference
            Series},
  volume = {10575},
  month = feb,
  eid = {105751M},
  pages = {105751M},
  doi = {10.1117/12.2293408},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10575E..1MW},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
},
  @article{anomaly_detection_defi,
  author = {Ul Hassan, Muneeb and Rehmani, Mubashir Husain and Chen, Jinjun},
  journal = {IEEE Communications Surveys \& Tutorials},
  title = {Anomaly Detection in Blockchain Networks: A Comprehensive Survey},
  year = {2023},
  volume = {25},
  number = {1},
  pages = {289-318},
  keywords = {Blockchains;Anomaly detection;Security;Smart
              contracts;Privacy;Bitcoin;Tutorials;Blockchain;anomaly
              detection;fraud detection},
  doi = {10.1109/COMST.2022.3205643},
}
,
  @article{anomaly_detection_manufacturing,
  AUTHOR = {Oh, Dong Yul and Yun, Il Dong},
  TITLE = {Residual Error Based Anomaly Detection Using Auto-Encoder in SMD
           Machine Sound},
  JOURNAL = {Sensors},
  VOLUME = {18},
  YEAR = {2018},
  NUMBER = {5},
  ARTICLE-NUMBER = {1308},
  URL = {https://www.mdpi.com/1424-8220/18/5/1308},
  PubMedID = {29695084},
  ISSN = {1424-8220},
  ABSTRACT = {Detecting an anomaly or an abnormal situation from given noise is
              highly useful in an environment where constantly verifying and
              monitoring a machine is required. As deep learning algorithms are
              further developed, current studies have focused on this problem.
              However, there are too many variables to define anomalies, and the
              human annotation for a large collection of abnormal data labeled at
              the class-level is very labor-intensive. In this paper, we propose
              to detect abnormal operation sounds or outliers in a very complex
              machine along with reducing the data-driven annotation cost. The
              architecture of the proposed model is based on an auto-encoder, and
              it uses the residual error, which stands for its reconstruction
              quality, to identify the anomaly. We assess our model using
              Surface-Mounted Device (SMD) machine sound, which is very complex,
              as experimental data, and state-of-the-art performance is
              successfully achieved for anomaly detection.},
  DOI = {10.3390/s18051308},
},
  @article{anomaly_detection_history,
  author = {F.Y. Edgeworth and},
  title = {XLI. On discordant observations },
  journal = {The London, Edinburgh, and Dublin Philosophical Magazine and
             Journal of Science},
  volume = {23},
  number = {143},
  pages = {364--375},
  year = {1887},
  publisher = {Taylor \& Francis},
  doi = {10.1080/14786448708628471},
  URL = { https://doi.org/10.1080/14786448708628471 },
  eprint = { https://doi.org/10.1080/14786448708628471 },
},
  @inproceedings{degradation_quantification_rain,
  author = {Zhang, Chen and Huang, Zefan and Ang, Marcelo H. and Rus, Daniela},
  booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and
               Systems (IROS)},
  title = {LiDAR Degradation Quantification for Autonomous Driving in Rain},
  year = {2021},
  volume = {},
  number = {},
  pages = {3458-3464},
  keywords = {Degradation;Location awareness;Laser radar;Rain;Codes;System
              performance;Current measurement},
  doi = {10.1109/IROS51168.2021.9636694},
},
  @article{deep_learning_overview,
  title = {Deep learning in neural networks: An overview},
  journal = {Neural Networks},
  volume = {61},
  pages = {85-117},
  year = {2015},
  issn = {0893-6080},
  doi = {https://doi.org/10.1016/j.neunet.2014.09.003},
  url = {https://www.sciencedirect.com/science/article/pii/S0893608014002135},
  author = {Jürgen Schmidhuber},
  keywords = {Deep learning, Supervised learning, Unsupervised learning,
              Reinforcement learning, Evolutionary computation},
  abstract = {In recent years, deep artificial neural networks (including
              recurrent ones) have won numerous contests in pattern recognition
              and machine learning. This historical survey compactly summarizes
              relevant work, much of it from the previous millennium. Shallow and
              Deep Learners are distinguished by the depth of their credit
              assignment paths, which are chains of possibly learnable, causal
              links between actions and effects. I review deep supervised
              learning (also recapitulating the history of backpropagation),
              unsupervised learning, reinforcement learning & evolutionary
              computation, and indirect search for short programs encoding deep
              and large networks.},
},
@article{autoencoder_survey,
  title = {A comprehensive survey on design and application of autoencoder in
           deep learning},
  journal = {Applied Soft Computing},
  volume = {138},
  pages = {110176},
  year = {2023},
  issn = {1568-4946},
  doi = {https://doi.org/10.1016/j.asoc.2023.110176},
  url = {https://www.sciencedirect.com/science/article/pii/S1568494623001941},
  author = {Pengzhi Li and Yan Pei and Jianqiang Li},
  keywords = {Deep learning, Autoencoder, Unsupervised learning, Feature
              extraction, Autoencoder application},
  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.},
}, 
@article{semi_overview,
  author = {Yang, Xiangli and Song, Zixing and King, Irwin and Xu, Zenglin},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  title = {A Survey on Deep Semi-Supervised Learning},
  year = {2023},
  volume = {35},
  number = {9},
  pages = {8934-8954},
  keywords = {Semisupervised learning;Training;Taxonomy;Task analysis;Deep
              learning;Data models;Supervised learning;Deep semi-supervised
              learning;semi-supervised learning;deep learning},
  doi = {10.1109/TKDE.2022.3220219},
}, 
@book{ai_fundamentals_book,
  title = {Fundamentals of Artificial Intelligence},
  url = {http://dx.doi.org/10.1007/978-81-322-3972-7},
  DOI = {10.1007/978-81-322-3972-7},
  publisher = {Springer India},
  author = {Chowdhary, K.R.},
  year = {2020},
  language = {en},
},

@article{machine_learning_overview,
  title = {Machine Learning from Theory to Algorithms: An Overview},
  volume = {1142},
  ISSN = {1742-6596},
  url = {http://dx.doi.org/10.1088/1742-6596/1142/1/012012},
  DOI = {10.1088/1742-6596/1142/1/012012},
  journal = {Journal of Physics: Conference Series},
  publisher = {IOP Publishing},
  author = {Alzubi, Jafar and Nayyar, Anand and Kumar, Akshi},
  year = {2018},
  month = nov,
  pages = {012012},
},

@article{machine_learning_first_definition,
  title = {Some Studies in Machine Learning Using the Game of Checkers},
  volume = {3},
  ISSN = {0018-8646},
  url = {http://dx.doi.org/10.1147/rd.33.0210},
  DOI = {10.1147/rd.33.0210},
  number = {3},
  journal = {IBM Journal of Research and Development},
  publisher = {IBM},
  author = {Samuel, A. L.},
  year = {1959},
  month = jul,
  pages = {210–229},
},
@inproceedings{bg_ad_pointclouds_scans,
  title = {Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
           },
  url = {http://dx.doi.org/10.1109/WACV56688.2023.00264},
  DOI = {10.1109/wacv56688.2023.00264},
  booktitle = {2023 IEEE/CVF Winter Conference on Applications of Computer
               Vision (WACV)},
  publisher = {IEEE},
  author = {Bergmann, Paul and Sattlegger, David},
  year = {2023},
  month = jan,
  pages = {2612–2622},
},
  @article{bg_ad_pointclouds_poles,
  title = {Automatic Detection and Classification of Pole-Like Objects in Urban
           Point Cloud Data Using an Anomaly Detection Algorithm},
  volume = {7},
  ISSN = {2072-4292},
  url = {http://dx.doi.org/10.3390/rs71012680},
  DOI = {10.3390/rs71012680},
  number = {10},
  journal = {Remote Sensing},
  publisher = {MDPI AG},
  author = {Rodríguez-Cuenca, Borja and García-Cortés, Silverio and Ordóñez,
            Celestino and Alonso, Maria},
  year = {2015},
  month = sep,
  pages = {12680–12703},
},

@article{semi_ad_survey,
  title = {Semi-supervised anomaly detection algorithms: A comparative summary
           and future research directions},
  volume = {218},
  ISSN = {0950-7051},
  url = {http://dx.doi.org/10.1016/j.knosys.2021.106878},
  DOI = {10.1016/j.knosys.2021.106878},
  journal = {Knowledge-Based Systems},
  publisher = {Elsevier BV},
  author = {Villa-Pérez, Miryam Elizabeth and Álvarez-Carmona, Miguel Á. and
            Loyola-González, Octavio and Medina-Pérez, Miguel Angel and
            Velazco-Rossell, Juan Carlos and Choo, Kim-Kwang Raymond},
  year = {2021},
  month = apr,
  pages = {106878},
},
@inbook{bg_autoencoder_ad,
  title = {Outlier Detection with Autoencoder Ensembles},
  ISBN = {9781611974973},
  url = {http://dx.doi.org/10.1137/1.9781611974973.11},
  DOI = {10.1137/1.9781611974973.11},
  booktitle = {Proceedings of the 2017 SIAM International Conference on Data
               Mining},
  publisher = {Society for Industrial and Applied Mathematics},
  author = {Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga,
            Deepak},
  year = {2017},
  month = jun,
  pages = {90–98},
},
@inproceedings{bg_autoencoder_ad_2,
  title = {Memorizing Normality to Detect Anomaly: Memory-Augmented Deep
           Autoencoder for Unsupervised Anomaly Detection},
  url = {http://dx.doi.org/10.1109/ICCV.2019.00179},
  DOI = {10.1109/iccv.2019.00179},
  booktitle = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  publisher = {IEEE},
  author = {Gong, Dong and Liu, Lingqiao and Le, Vuong and Saha, Budhaditya and
            Mansour, Moussa Reda and Venkatesh, Svetha and Van Den Hengel, Anton},
  year = {2019},
  month = oct,
  pages = {1705–1714},
},
@article{bg_autoencoder_lidar,
  title = {Deep Learning Approach for Building Detection Using LiDAR–Orthophoto
           Fusion},
  volume = {2018},
  ISSN = {1687-7268},
  url = {http://dx.doi.org/10.1155/2018/7212307},
  DOI = {10.1155/2018/7212307},
  journal = {Journal of Sensors},
  publisher = {Wiley},
  author = {Nahhas, Faten Hamed and Shafri, Helmi Z. M. and Sameen, Maher
            Ibrahim and Pradhan, Biswajeet and Mansor, Shattri},
  year = {2018},
  month = aug,
  pages = {1–12},
},
@article{lidar_denoising_survey,
  title = {LiDAR Denoising Methods in Adverse Environments: A Review},
  volume = {25},
  ISSN = {2379-9153},
  url = {http://dx.doi.org/10.1109/JSEN.2025.3526175},
  DOI = {10.1109/jsen.2025.3526175},
  number = {5},
  journal = {IEEE Sensors Journal},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Park, Ji-Il and Jo, SeungHyeon and Seo, Hyung-Tae and Park, Jihyuk},
  year = {2025},
  month = mar,
  pages = {7916–7932},
},
@inproceedings{lidar_subt_dust_removal,
  title = {Efficient Real-time Smoke Filtration with 3D LiDAR for Search and
           Rescue with Autonomous Heterogeneous Robotic Systems},
  url = {http://dx.doi.org/10.1109/IECON51785.2023.10312303},
  DOI = {10.1109/iecon51785.2023.10312303},
  booktitle = {IECON 2023- 49th Annual Conference of the IEEE Industrial
               Electronics Society},
  publisher = {IEEE},
  author = {Kyuroson, Alexander and Koval, Anton and Nikolakopoulos, George},
  year = {2023},
  month = oct,
  pages = {1–7},
},
@article{lidar_denoising_dust,
  title = {Dust De-Filtering in LiDAR Applications With Conventional and CNN
           Filtering Methods},
  volume = {12},
  ISSN = {2169-3536},
  url = {http://dx.doi.org/10.1109/ACCESS.2024.3362804},
  DOI = {10.1109/access.2024.3362804},
  journal = {IEEE Access},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Parsons, Tyler and Seo, Jaho and Kim, Byeongjin and Lee, Hanmin and
            Kim, Ji-Chul and Cha, Moohyun},
  year = {2024},
  pages = {22032–22042},
},
  @inproceedings{lidar_errormodel_particles,
  author = {Mokrane, Hadj-Bachir and De Souza, Philippe and Nordqvist, P and Roy
            , N},
  year = {2021},
  month = {05},
  pages = {},
  title = {Modelling of LIDAR sensor disturbances by solid airborne particles},
},
@article{lidar_errormodel_automotive,
  title = {A Survey on Sensor Failures in Autonomous Vehicles: Challenges and
           Solutions},
  volume = {24},
  ISSN = {1424-8220},
  url = {http://dx.doi.org/10.3390/s24165108},
  DOI = {10.3390/s24165108},
  number = {16},
  journal = {Sensors},
  publisher = {MDPI AG},
  author = {Matos, Francisco and Bernardino, Jorge and Durães, João and Cunha,
            João},
  year = {2024},
  month = aug,
  pages = {5108},
},
@article{lidar_errormodel_consensus,
  title = {Present and Future of SLAM in Extreme Environments: The DARPA SubT
           Challenge},
  volume = {40},
  ISSN = {1941-0468},
  url = {http://dx.doi.org/10.1109/TRO.2023.3323938},
  DOI = {10.1109/tro.2023.3323938},
  journal = {IEEE Transactions on Robotics},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Ebadi, Kamak and Bernreiter, Lukas and Biggie, Harel and Catt, Gavin
            and Chang, Yun and Chatterjee, Arghya and Denniston, Christopher E.
            and Desch\^enes, Simon-Pierre and Harlow, Kyle and Khattak, Shehryar
            and Nogueira, Lucas and Palieri, Matteo and Petráček, Pavel and
            Petrlík, Matěj and Reinke, Andrzej and Krátký, Vít and Zhao, Shibo
            and Agha-mohammadi, Ali-akbar and Alexis, Kostas and Heckman,
            Christoffer and Khosoussi, Kasra and Kottege, Navinda and Morrell,
            Benjamin and Hutter, Marco and Pauling, Fred and Pomerleau, Fran\c{c}
            ois and Saska, Martin and Scherer, Sebastian and Siegwart, Roland and
            Williams, Jason L. and Carlone, Luca},
  year = {2024},
  pages = {936–959},
},
@article{when_the_dust_settles,
  title = {When the Dust Settles: The Four Behaviors of LiDAR in the Presence of
           Fine Airborne Particulates},
  volume = {34},
  ISSN = {1556-4967},
  url = {http://dx.doi.org/10.1002/rob.21701},
  DOI = {10.1002/rob.21701},
  number = {5},
  journal = {Journal of Field Robotics},
  publisher = {Wiley},
  author = {Phillips, Tyson Govan and Guenther, Nicky and McAree, Peter Ross},
  year = {2017},
  month = feb,
  pages = {985–1009},
},
@misc{odds,
  author = {Shebuti Rayana},
  year = {2016},
  title = {ODDS Library},
  url = {https://odds.cs.stonybrook.edu},
  institution = {Stony Brook University, Department of Computer Sciences},
}




