Paper Citation Record · LEDGER

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion

As of 23 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.05811.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.05811 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T23:00:55.252356Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bba4267-7af6-4014-9796-68523b6fdd7e · outbound

This paper cites Deep industrial image anomaly detection: A survey,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Deep industrial image anomaly detection: A survey,

Reference 1

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Observation 320f0cc6-1979-41cf-80c9-2751baefe9b9 · outbound

This paper cites Machine learning for anomaly assessment in sensor networks for ndt in aerospace,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Machine learning for anomaly assessment in sensor networks for ndt in aerospace,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.765365Z

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Observation c6da4d52-c385-4f2c-9afe-18eae3f9a106 · outbound

This paper cites A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.749794Z

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Observation d42d18c8-bb59-45e6-ae2c-b5623f741083 · outbound

This paper cites Multimodal anomaly detection based on deep auto-encoder for object slip perception of mobile ma- nipulation robots,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Multimodal anomaly detection based on deep auto-encoder for object slip perception of mobile ma- nipulation robots,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.733841Z

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Observation d68df5b4-358d-4287-925f-6408bb8a1ca1 · outbound

This paper cites Safe robot navigation via multi-modal anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Safe robot navigation via multi-modal anomaly detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.719128Z

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Observation 5ce07a7a-92e4-4049-bfa4-45f048c61c7d · outbound

This paper cites Proactive anomaly detection for robot navigation with multi-sensor fusion,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Proactive anomaly detection for robot navigation with multi-sensor fusion,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.706876Z

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Observation 95eba5b2-0030-472b-af7c-43c3ba4c4b0a · outbound

This paper cites Safe autonomous driving in adverse weather: Sensor evaluation and performance monitoring,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Safe autonomous driving in adverse weather: Sensor evaluation and performance monitoring,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.693697Z

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Observation aaa66450-85fd-4d96-94b1-612b12a5798c · outbound

This paper cites A novel vehicle collision detection system: Integrating audio-visual fusion for enhanced performance,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A novel vehicle collision detection system: Integrating audio-visual fusion for enhanced performance,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.680237Z

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Observation a42d1417-cf62-4597-a5a6-378f77cd30bc · outbound

This paper cites Fault diagnosis for mobile robots based on spatial-temporal graph attention network under imbalanced data,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Fault diagnosis for mobile robots based on spatial-temporal graph attention network under imbalanced data,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.666780Z

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Observation fbd67623-512e-4c2e-843a-902283d5e10d · outbound

This paper cites A semi-supervised enhanced fault diagnosis algorithm for complex equipment assisted by digital multitwins,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A semi-supervised enhanced fault diagnosis algorithm for complex equipment assisted by digital multitwins,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.652435Z

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Observation df569d87-dc0c-453b-947c-3a87def97813 · outbound

This paper cites A few-shot machinery fault diagnosis framework based on self-supervised signal representation learning,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A few-shot machinery fault diagnosis framework based on self-supervised signal representation learning,

Reference 11

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Observation 9e2e17d0-62c1-474a-8bfe-795230ef4894 · outbound

This paper cites Modeling the distribution of normal data in pre-trained deep features for anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Modeling the distribution of normal data in pre-trained deep features for anomaly detection,

Reference 12

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Observation 670a7f7a-8ea2-4702-987a-206677a66a03 · outbound

This paper cites The voraus-ad dataset for anomaly detection in robot applications,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion The voraus-ad dataset for anomaly detection in robot applications,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.601340Z

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Observation 45f46bb3-a6ad-4cdb-82a0-dc3afdf132ba · outbound

This paper cites Reconstruction-based deep unsupervised adaptive threshold support vector data description for wind turbine anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Reconstruction-based deep unsupervised adaptive threshold support vector data description for wind turbine anomaly detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.587733Z

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Observation 7040c467-1570-4ba0-9174-2a7131d0e14a · outbound

This paper cites A novel methodology for unsupervised anomaly detection in industrial electrical systems,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A novel methodology for unsupervised anomaly detection in industrial electrical systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.571549Z

Source-reported events for the cited work

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Observation a73425a9-5a27-410b-9268-3290c2b9469d · outbound

This paper cites Data-driven anomaly detection using ocsvm with boundary optimzation,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Data-driven anomaly detection using ocsvm with boundary optimzation,

Reference 16

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Observation a8d751e2-7da6-417c-8f94-d0d560c825db · outbound

This paper cites Calibrated one-class classification for unsupervised time series anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Calibrated one-class classification for unsupervised time series anomaly detection,

Reference 17

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Observation ad4e9775-7d65-475c-83f2-d65d057a6c1c · outbound

This paper cites Pyramid reconstruction assisted deep autoencoding gaussian mixture model for industrial fault detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Pyramid reconstruction assisted deep autoencoding gaussian mixture model for industrial fault detection,

Reference 18

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Observation dd57796a-1d35-4236-bdb2-51db4ad8a5ee · outbound

This paper cites Unsuper- vised anomaly detection for self-flying delivery drones,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Unsuper- vised anomaly detection for self-flying delivery drones,

Reference 19

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Observation db1b1328-e6ee-4b7b-9ddb-36c0d7b0230b · outbound

This paper cites Robust spatial-temporal autoencoder for unsupervised anomaly detection of unmanned aerial vehicle with flight data,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Robust spatial-temporal autoencoder for unsupervised anomaly detection of unmanned aerial vehicle with flight data,

Reference 20

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Observation 3ee088a6-12f5-4751-8797-c8af13b39427 · outbound

This paper cites Support vector data description,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Support vector data description,

Reference 21

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Observation df9d64df-8064-4df9-bba9-4757c968c24b · outbound

This paper cites Deep one-class classification,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Deep one-class classification,

Reference 22

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Observation 6d48f521-8b63-4e98-846f-9895e27a9083 · outbound

This paper cites Patch svdd: Patch-level svdd for anomaly detection and segmentation,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Patch svdd: Patch-level svdd for anomaly detection and segmentation,

Reference 23

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Observation bc755189-3b24-4116-b6f2-e24d89cdcd38 · outbound

This paper cites Is someone speaking? exploring long-term temporal features for audio- visual active speaker detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Is someone speaking? exploring long-term temporal features for audio- visual active speaker detection,

Reference 24

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Observation f59242ec-a081-43b7-a011-8c9625ad1f5d · outbound

This paper cites A fast algorithm for the minimum covariance determinant estimator,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion A fast algorithm for the minimum covariance determinant estimator,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.455421Z

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Observation 1e643204-e6eb-4a66-b852-7d071433d6cc · outbound

This paper cites Usad: Unsupervised anomaly detection on multivariate time series,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Usad: Unsupervised anomaly detection on multivariate time series,

Reference 26

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Observation fd214e54-910c-43f9-aa47-9999c7146395 · outbound

This paper cites Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,

Reference 27

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Observation 8dacaac9-8a7b-446b-bd07-586ba4f62065 · outbound

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Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Isolation forest,

Reference 28

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Observation e280419b-2fde-4334-b029-e023de29a291 · outbound

This paper cites One-class svms for document clas- sification,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion One-class svms for document clas- sification,

Reference 29

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Observation be5ea6fa-0b60-49ed-9be7-ac9dd439c2b9 · outbound

This paper cites Deep isolation forest for anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Deep isolation forest for anomaly detection,

Reference 30

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This paper cites Deep autoencoding gaussian mixture model for unsupervised anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Deep autoencoding gaussian mixture model for unsupervised anomaly detection,

Reference 31

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This paper cites Vae-based deep svdd for anomaly detection,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Vae-based deep svdd for anomaly detection,

Reference 32

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Observation f028460e-5045-49ff-bcfc-2583b67a6b66 · outbound

This paper cites MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

Reference 33

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Observation c7003e60-0096-477f-94e5-6998c8eee1bc · outbound

This paper cites Swat: A water treatment testbed for research and training on ics security,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Swat: A water treatment testbed for research and training on ics security,

Reference 34

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Observation 1bae6449-cfa1-4cef-91f8-af0fbbda6de6 · outbound

This paper cites Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,

Reference 35

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Observation 6d85ef08-fe07-4c85-b9cd-5c2418d9ff9f · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 36

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Observation 7eb4a8de-9ae7-429c-af67-993341c2b456 · outbound

This paper cites TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data,

Reference 37

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This paper cites Angle-based outlier de- tection in high-dimensional data,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Angle-based outlier de- tection in high-dimensional data,

Reference 38

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Observation 6b29fa99-0a31-4835-bfe0-e2b8da78d781 · outbound

This paper cites Robospike: Fully utilizing the heterogeneous system with subcallback scheduling in ros 2,.

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion Robospike: Fully utilizing the heterogeneous system with subcallback scheduling in ros 2,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:00:55.327342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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