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

arXiv: https://arxiv.org/abs/2504.19002 · HTML: https://mlsanity.com/paper/2504.19002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving](https://mlsanity.com/paper/2310.07602/) (2023)
- [Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology](https://mlsanity.com/paper/2406.00490/) (2024)
- [Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning](https://mlsanity.com/paper/2501.12799/) (2025)
- [Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors](https://mlsanity.com/paper/2407.08049/) (2024)
- [Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations](https://mlsanity.com/paper/2408.13742/) (2024)
- [Enhancing State Estimator for Autonomous Racing : Leveraging Multi-modal System and Managing Computing Resources](https://mlsanity.com/paper/2308.07173/) (2023)
- [Towards Autonomous Multi-Modal Mobility Morphobot (M4) Robot: Traversability Estimation and 3D Path Planning](https://mlsanity.com/paper/2308.13972/) (2023)

## Built on by (most cited first, 1 of 1)

- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)

## Abstract (arXiv)

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.