Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation (2504.19002v1)

Abstract: This paper introduces a novel deep learning-based multimodal fusion architecture aimed at enhancing the perception capabilities of autonomous navigation robots in complex environments. By utilizing innovative feature extraction modules, adaptive fusion strategies, and time-series modeling mechanisms, the system effectively integrates RGB images and LiDAR data. The key contributions of this work are as follows: a. the design of a lightweight feature extraction network to enhance feature representation; b. the development of an adaptive weighted cross-modal fusion strategy to improve system robustness; and c. the incorporation of time-series information modeling to boost dynamic scene perception accuracy. Experimental results on the KITTI dataset demonstrate that the proposed approach increases navigation and positioning accuracy by 3.5% and 2.2%, respectively, while maintaining real-time performance. This work provides a novel solution for autonomous robot navigation in complex environments.

• The paper introduces a dual-stream feature extraction module combining a CNN-Transformer for RGB images and an enhanced PointNet++ for LiDAR data.
• It employs an adaptive cross-modal fusion strategy with dynamic weight allocation to improve robustness under adverse sensing conditions.
• Temporal modeling via LSTM/GRU networks boosts navigation accuracy, achieving real-time performance with a 3.5% and 2.2% improvement in key metrics.

Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation

The paper "Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation" proposes a novel architecture to enhance the perception and navigation capabilities of autonomous robots in complex environments. This architecture employs a deep learning-based multimodal fusion strategy integrating RGB images and LiDAR data, addressing challenges faced by traditional single-modal perception systems which falter in dynamic and unpredictable settings.

Key Contributions

The proposed approach introduces three main components:

1. Feature Extraction Module: A dual-stream network is developed to improve feature quality for different modalities. For RGB images, an innovative CNN+Transformer hybrid architecture is used to extract visual details and semantic features, overcoming issues such as gradient vanishing. For LiDAR data, enhancements to the PointNet++ framework optimize point cloud processing, focusing on dynamic point sampling and feature aggregation.
2. Adaptive Cross-Modal Fusion Strategy: The architecture employs a dynamic weight allocation mechanism, adjusting the importance of features from various modalities based on their reliability. This adaptive fusion aims to improve robustness, particularly in adverse conditions where sensor data quality may vary.
3. Temporal Modeling: By leveraging LSTM/GRU networks, the system processes temporal dependencies and predicts dynamic scene changes, integrating historical data with current observations to offer improved navigation decisions.

Experimental Results

The system was evaluated using the KITTI dataset, performing rigorous tests against established benchmarks. Empirical results report improvements in navigation accuracy and localization precision by 3.5% and 2.2%, respectively, compared to previous methodologies. These enhancements are achieved while maintaining real-time performance requirements, with the system processing at least 20 FPS and demonstrating robust operation in scenarios with variable illumination and dynamic objects.

Ablation Study & Scenario Analysis

Detailed ablation experiments showcase the effectiveness of individual modules like feature extraction, fusion strategy, and temporal modeling. Each contributes significantly to overall performance, particularly under complex dynamic conditions. A scenario analysis reveals robustness in environments featuring dynamic obstacles and adverse weather, further emphasizing the practical applicability of the proposed solutions.

Computational Efficiency

The architecture displays computational efficiency by optimizing memory usage across its functional modules. Improvements in feature extraction and fusion strategies result in reduced memory demands compared to existing methods, making it viable for deployment on hardware with constrained resources.

Implications and Future Directions

This research marks significant advancements in the field of autonomous robot navigation, enhancing capabilities to perceive and operate reliably in diverse and challenging environments. The integration of multimodal fusion and temporal modeling offers a framework that is versatile and scalable, with potential applications extending into areas like autonomous driving and mobile robotics. Going forward, it may be worthwhile to explore further optimizations in sensor data processing and integration of additional modalities to enhance perception accuracy and responsiveness further. The adaptability of this architecture suggests promising developments in AI-driven autonomous systems, paving the way for broader applicability in real-world scenarios across different domains.