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A Generalized Visual SLAM Enhancement Method Using Maximum Texture Distribution Entropy
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AI
DOI：10.1109/TII.2025.3584475.png)
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Abstract
En 中文
This article presents a generalized method for enhancing visual simultaneous localization and mapping (SLAM) by leveraging the maximum information entropy in texture distribution. Traditionally, visual SLAM methods are based on the assumption of independent and identically distributed normal models for observed errors, uniformly minimizing the combined errors to estimate the states, while failing to account for the information differences between individual observations, which may hinder achieving ideal results. In response to this, we introduce the texture distribution entropy to quantify the information content of each feature, where higher entropy indicates greater importance. Then, the conventional state estimation strategy is improved by assigning weights to points based on their importance, promoting a balance between the contributions of regions with various texture densities, thereby enhancing the reliability of the results under uneven observations. Moreover, based on the maximum distribution entropy of texture features, a novel keyframe decision strategy is proposed that fully evaluates the utilization of current texture information and the richness of the scene texture, ensuring effective and timely keyframe construction. The proposed method is highly generalizable and can be applied to a wide range of visual SLAM systems, provided they are based on texture feature-based state estimation or keyframe decision. Finally, experimental results on public datasets demonstrate that, compared to recent state-of-the-art visual SLAM methods, our method significantly improves the accuracy of trajectory tracking estimation.
Keywords:
Information entropy
keyframe decision
state estimation
texture distribution
visual simultaneous localization and mapping (SLAM)
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