DT-SLAM: Dynamic Thresholding Based Corner Point Extraction in SLAM System

Visual localization estimation is highly depended on the quality of video frames or captured images. Estimation quality may be affected by the poor visibility, low background texture and overexposure. Low quality frames with blurred edges and poor contrast pose tremendous difficulties for corner point detection in SLAM impacting the overall accuracy of estimation. This paper introduces DT-SLAM, a dynamic self-adaptive threshold (DSAT) approach for ORB corner point extraction in FAST to improve SLAM's localization performance. The proposed method replaces the existing static threshold-based ORB extraction approach, enabling improved performance in complex real-world scenes. In addition, this study introduces a threshold switching mechanism (TSM) to replace the existing SLAM's frame-level and cell-level thresholds for corner point extraction. The proposed DT-SLAM approach is validated using the TUM RGB-D and EuRoC benchmark datasets for location tracking performances. The results indicate that the proposed DT-SLAM outperforms the current state-of-the-art ORB-SLAM3, especially in challenging environments.