Low-Texture 환경에서 적응형 이미지 증강 및 임계값 조정을 통한 ORB-SLAM 성능 향상
Improving ORB-SLAM Performance in Low-Texture Environments through Adaptive Image Augmentation and Threshold Adjustment
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This paper proposes adaptive image augmentation and preprocessing techniques to improve the performance of the ORB-SLAM2 system in low-texture environments. In such environments, ORB-SLAM2 often experiences degradation in pose estimation accuracy and system stability. The proposed method analyzes the texture characteristics of images using a Laplacian mask, dynamically adjusting sharpness and contour extraction to effectively enhance structural details in low-texture images. Additionally, an adaptive ORB feature thresholding mechanism is introduced to prevent excessive feature extraction caused by image augmentation, thereby improving accuracy in both low- and high-texture regions. Experimental results show that the proposed method significantly increases the number of ORB features, the number of matched features between consecutive frames, and the proportion of frames in a stable tracking state. These improvements are particularly pronounced in low-texture environments, enhancing the overall stability and accuracy of the ORB-SLAM system.
Abstract
This paper proposes adaptive image augmentation and preprocessing techniques to improve the performance of the ORB-SLAM2 system in low-texture environments. In such environments, ORB-SLAM2 often experiences degradation in pose estimation accuracy and system stability. The proposed method analyzes the texture characteristics of images using a Laplacian mask, dynamically adjusting sharpness and contour extraction to effectively enhance structural details in low-texture images. Additionally, an adaptive ORB feature thresholding mechanism is introduced to prevent excessive feature extraction caused by image augmentation, thereby improving accuracy in both low- and high-texture regions. Experimental results show that the proposed method significantly increases the number of ORB features, the number of matched features between consecutive frames, and the proportion of frames in a stable tracking state. These improvements are particularly pronounced in low-texture environments, enhancing the overall stability and accuracy of the ORB-SLAM system.