Abstract
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.
Highlights
T HE ability for an agent to localize and track its movement through an otherwise unmapped environment in application areas such as autonomous robotics, self-driving cars and Unmanned Aerial Vehicles (UAV)
The ubiquity of highperformance processors and high-resolution color cameras had seen the popularization of visual Simultaneous Localization and Mapping (SLAM). vSLAM applies visual odometry (VO) [1], [2] to determine the position and orientation of an observer using the camera images
Fig. 4 shows the comparison of detected corner points by Oriented Fast and Rotated Brief (ORB)-SLAM and dynamic threshold SLAM (DT-SLAM) using two consecutive input frames obtained from the EuRoC V202 sequence
Summary
T HE ability for an agent to localize and track its movement through an otherwise unmapped environment in application areas such as autonomous robotics, self-driving cars and Unmanned Aerial Vehicles (UAV). Often these agents operate in environments that are either not mapped or cannot be mapped practically e.g., home-based environments. The steps included individual features matching with fast nearestneighbor algorithm, followed by cluster identification with Hough transform and lastly verification with least-squares method for object matching. Those methods were either unstable or time-consuming which were not practical
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