用注意力和边缘信息优化视觉定位的特征选择
Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization
- 通过空间注意力聚焦图像中关键区域，提升特征选择效率
- 结合边缘检测，使选中的特征位于鲁棒区域，提高2D-3D匹配精度
- 在室外数据集上表现优于现有方法，适合机器人定位场景
视觉定位通过视觉数据确定智能体在环境中的精确位置与朝向，是机器人领域尤其是自主导航中的关键技术。利用低成本传感器如RGB相机即可实现姿态估计。当前方法多采用场景坐标回归来推断姿态，但需对整幅图像进行2D-3D对应关系回归，而并非所有图像区域都提供有效信息。为此，本文提出一种注意力网络，用于识别图像中最具信息量的区域，筛选出得分最高的特征，并与边缘检测结果融合。该融合策略确保训练缓冲区中的特征位于结构稳定的区域，从而增强2D-3D对应关系，提升整体定位性能。实验在室外基准数据集上验证了本方法的优越性。
原文摘要 · Abstract (English)
Visual localization determines an agent's precise position and orientation within an environment using visual data. It has become a critical task in the field of robotics, particularly in applications such as autonomous navigation. This is due to the ability to determine an agent's pose using cost-effective sensors such as RGB cameras. Recent methods in visual localization employ scene coordinate regression to determine the agent's pose. However, these methods face challenges as they attempt to regress 2D-3D correspondences across the entire image region, despite not all regions providing useful information. To address this issue, we introduce an attention network that selectively targets informative regions of the image. Using this network, we identify the highest-scoring features to improve the feature selection process and combine the result with edge detection. This integration ensures that the features chosen for the training buffer are located within robust regions, thereby improving 2D-3D correspondence and overall localization performance. Our approach was tested on the outdoor benchmark dataset, demonstrating superior results compared to previous methods.
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