• 版本
• 下载 1
• 文件大小 3.88 MB
• 文件计数 1
• 创建日期 2020年8月29日
• 最后更新 2020年8月29日

StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks

Robust point cloud registration in real-time is an important prerequisite for many
mapping and localization algorithms. Traditional methods like ICP and its derivatives tend to fail without good initialization, insufficient overlap or in the presence
of dynamic objects. We overcome these drawbacks by introducing StickyPillars,
an end-to-end trained 3D feature matching approach based on a graph neural network. We perform context aggregation with the aid of transformer based multi-head
self and cross attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. In contrast to
state-of-the-art matching methods, our system does not rely on hand crafted feature
descriptors or heuristic matching strategies. Our method outperforms state-of-the
art matching algorithms like ICP while being suitable for real-time robotics applications. In particular we demonstrate this capability by comparing the translational
and rotational error of reconstructed relative poses between two point clouds.