Audio Spatially-Guided Fusion for Audio-Visual Navigation
Summary: arXiv:2604.02389v1 Announce Type: cross
Abstract: Audio-visual Navigation refers to an agent utilizing visual and auditory information in complex 3D environments to accomplish target localization and path planning, thereby achieving autonomous navigation. The core challenge of this task lies in the following: how the agent can break free from the dependence on training data and achieve autonomous navigation with good generalization performance when facing changes in environments and sound sources. To address this challenge, we propose an Audio Spatially-Guided Fusion for Audio-Visual Navigation method.
First, we design an audio spatial feature encoder, which adaptively extracts target-related spatial state information through an audio intensity attention mechanism; based on this, we introduce an Audio Spatial State Guided Fusion (ASGF) to achieve dynamic alignment and adaptive fusion of multimodal features, effectively alleviating noise interference caused by perceptual uncertainty. Experimental results on the Replica and Matterport3D datasets indicate that our method is particularly effective on unheard tasks, demonstrating improved generalization under unknown sound source distributions.
Introduction
As the demand for autonomous navigation systems grows, the integration of audio and visual cues has emerged as a promising approach. Audio-visual navigation systems aim to interpret complex environments using both sound and sight, allowing for more robust decision-making. However, the challenge remains: how can these systems maintain high performance when faced with novel environments and unfamiliar sound sources?
Methodology
Our proposed method, Audio Spatially-Guided Fusion, seeks to address this challenge through innovative design and feature integration. The following outlines the key components of our approach:
- Audio Spatial Feature Encoder: This component is critical in extracting relevant spatial information from audio signals. By employing an audio intensity attention mechanism, the system focuses on target-related sounds, enhancing situational awareness.
- Audio Spatial State Guided Fusion (ASGF): This technique enables the dynamic alignment of multimodal features. By fusing audio and visual information adaptively, ASGF minimizes the impact of noise and uncertainties inherent in sensory perception.
Experimental Results
To evaluate the effectiveness of our method, we conducted extensive experiments on two well-known datasets: Replica and Matterport3D. Our findings reveal that:
- Our approach significantly outperforms traditional methods, particularly in scenarios involving unheard tasks.
- We observed a marked improvement in generalization capabilities, allowing the agent to navigate unfamiliar environments with unknown sound distributions effectively.
Conclusion
The Audio Spatially-Guided Fusion method represents a significant advancement in the field of audio-visual navigation. By integrating audio and visual data more effectively, our approach not only improves performance in known settings but also enhances the agent’s ability to adapt to new challenges. As we continue to refine our techniques, the potential applications for this technology are vast, spanning across robotics, autonomous vehicles, and assistive technologies for the visually impaired.