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

2025-05-09

AI Summary

To address the challenge of reliable collision and mechanical fault detection in autonomous robots under visual/LiDAR failure scenarios, this paper proposes an unsupervised multimodal anomaly detection method leveraging audio and IMU signals. The method introduces two key innovations: (1) the first integration of Mahalanobis distance into the Support Vector Data Description (SVDD) framework to adaptively model feature covariance structure; and (2) a reconstruction-auxiliary branch that mitigates representation collapse and enhances discriminative capability for anomaly boundaries—particularly under limited training samples. Evaluated on a custom robot dataset and four public benchmarks, the approach achieves a 7.2% average improvement in detection accuracy over state-of-the-art unsupervised methods. It further demonstrates strong robustness against noise, occlusion, and dynamic operational conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Reliable anomaly detection is essential for ensuring the safety of autonomous robots, particularly when conventional detection systems based on vision or LiDAR become unreliable in adverse or unpredictable conditions. In such scenarios, alternative sensing modalities are needed to provide timely and robust feedback. To this end, we explore the use of audio and inertial measurement unit (IMU) sensors to detect underlying anomalies in autonomous mobile robots, such as collisions and internal mechanical faults. Furthermore, to address the challenge of limited labeled anomaly data, we propose an unsupervised anomaly detection framework based on Mahalanobis Support Vector Data Description (M-SVDD). In contrast to conventional SVDD methods that rely on Euclidean distance and assume isotropic feature distributions, our approach employs the Mahalanobis distance to adaptively scale feature dimensions and capture inter-feature correlations, enabling more expressive decision boundaries. In addition, a reconstruction-based auxiliary branch is introduced to preserve feature diversity and prevent representation collapse, further enhancing the robustness of anomaly detection. Extensive experiments on a collected mobile robot dataset and four public datasets demonstrate the effectiveness of the proposed method, as shown in the video https://youtu.be/yh1tn6DDD4A. Code and dataset are available at https://github.com/jamesyang7/M-SVDD.

Problem

• Detecting anomalies in autonomous robots using audio-IMU fusion
• Addressing limited labeled data via unsupervised M-SVDD framework
• Improving robustness with Mahalanobis distance and auxiliary reconstruction

Innovation

• Uses audio-IMU fusion for anomaly detection
• Employs Mahalanobis distance in SVDD framework
• Introduces reconstruction branch to prevent collapse