Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation
This work addresses the problem of robust perception for autonomous robots in complex environments, but it is incremental as it builds on existing multimodal fusion methods with specific improvements.
This paper tackled the problem of enhancing perception for autonomous navigation robots in complex environments by introducing a deep learning-based multimodal fusion architecture, resulting in a 3.5% increase in navigation accuracy and a 2.2% increase in positioning accuracy on the KITTI dataset while maintaining real-time performance.
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.