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Unsupervised Machine Learning Based Scalable Fusion for Active Perception

The fusion of sensor data is an essential requirement for active perception in autonomous robots in order to accurately perceive an environment, make appropriate adjustments that improve the understanding of the environment and generate actionable insights. Despite a large body of literature in this domain, only a handful of research focuses on unsupervised machine learning, which is increasingly important in unlabeled, unknown environments. This paper proposes a scalable self-organizing neural architecture for environmental perception based on multimodal fusion using unsupervised machine learning. Inspired by the biological counterpart, the neural architecture consists of topographic maps across each modality and incorporates a novel scalable self-organization process that handles high volume, high-velocity sensor data. The co-occurrence relationships across modalities are captured in cross-modal connections and the reverse Hebbian projection is used for multimodal representation. This proposed unsupervised machine learning-based scalable fusion technique was implemented on Apache Hadoop and Apache Spark. It was validated using an extensive multimodal human activity sensor dataset to demonstrate efficient representation and multimodal fusion for active perception. Note to Practitioners—Heterogeneous sensors are increasingly used to capture different aspects of an event or an environment. This paper presents a multimodal fusion mechanism to fuse such heterogeneous sensor data to support the accurate perception of the surrounding. The proposed mechanism uses natural regularities in patterns observed across multiple modalities and enriches each modality with information from other co-occurring modalities to achieve a multimodal clustering. The cluster can be used for online prediction and offline action planning of the robot by matching the current sensory inputs. The algorithm uses unsupervised machine learning, and hence, can be used with any multimodal dataset without being restricted by the labeling of the dataset. In addition, we adapt the algorithm for multiple big data platforms so that it is effective and pragmatic for large-scale sensor data fusion tasks.