An Illumination-Adaptive Visual SLAM Algorithm With Lightweight Object Detection for Complex Dynamic Environments

Existing visual simultaneous localization and mapping (SLAM) algorithms excel in stable illumination variation and static environments, but struggle in dynamic environments with varying illumination. Object detection, an essential component for dynamic feature removal, imposes substantial computational burdens due to model architectural complexity. This study proposed a robust visual SLAM framework that fuses an illumination-adaptive thresholding feature point extraction module with a lightweight object detection network (You Only Look Once (YOLO) + ShuffleNetv2+ EMA) to effectively separate dynamic and static features by improving feature point extraction and matching accuracy. The fusion module is deployed at the SLAM front-end and utilizes an independent threading mechanism to decouple the dynamic feature point removal process from the main pipeline. Furthermore, a back-end point cloud mapping module is introduced into the SLAM framework, which collaborates with an RGB-D camera and the YOLO + ShuffleNetv2 + EMA network to eliminate dynamic object point clouds. Experimental results on indoor and outdoor datasets demonstrate that the proposed SLAM framework significantly enhances feature point processing capability and localization accuracy under varying illumination and dynamic environments. Under illumination conditions varying from 30% to 130% within the same scene, the adaptive thresholding method consistently maintains an average feature point count exceeding 1700, with an average variation of 784 feature points across different environments. The lightweight object detection network achieves a 70.72% reduction in parameter count and a 71.33% decrease in model size. In terms of localization accuracy, the framework achieves a maximum absolute trajectory error (ATE) reduction of over 95% and a maximum sum of squared errors (SSE) reduction of over 94%. The trajectories generated by the proposed SLAM algorithm closely align with the ground truth and enable real-time computation using CPU-only processing.