Year: 2025
Type: article
Abstract: Nowadays, people have high requirements for the robustness of autonomous driving. As a key technology in the field of autonomous driving, SLAM requires a lightweight and explainable algorithm framework when dealing with perception degradation scenes. At present, most multi-sensor fusion SLAM algorithms use methods such as training neural networks or adding new odometry constraints to achieve anti-interference, but these methods do not meet the requirements. We use the data communication technology of the ROS (Robot Operating System) platform to integrate the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm, the LiDAR Noise Filtering algorithm, and RTAB-Map (a multi-sensor fusion algorithm based on graph optimization). We then conducted experiments in three designed perception degradation scenes and used the MME indicator to quantify the experiments. The results showed that the MME of our framework in the perception-degraded environment was reduced by an average of 0.107, proving that the framework we proposed performs bet-ter than RTAB-Map in dealing with some perception degradation scenes.
DOI: 10.14419/g10zn898
Institution: Panyapiwat Institute of Management
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413441452