Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation
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.
Cite
@article{arxiv.2504.19002,
title = {Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation},
author = {Delun Lai and Yeyubei Zhang and Yunchong Liu and Chaojie Li and Huadong Mo},
journal= {arXiv preprint arXiv:2504.19002},
year = {2025}
}
Comments
6 pages, 4 figures