论文标题
BoxGraph：3D激光雷达的语义场所识别和姿势估计
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
论文作者
论文摘要
本文使用基于实例分割和图形匹配的LiDAR Point Clouds进行了极强和轻巧的定位。我们将3D点云建模为完全连接的语义识别组件的图形，每个顶点对应于对象实例并编码其形状。跨图的最佳顶点关联允许通过测量相似性进行完整的6度自由（DOF）姿势估计和放置识别。这种表示非常简洁，将地图的大小缩合为25倍，而最先进的图像只需要3KB代表1.4MB激光扫描。我们验证了系统在Semantickitti数据集上的功效，在该数据集中，我们获得了新的最先进的识别，平均召回了88.4％的召回，即下一个最接近的竞争对手以64.9％的速度进行。我们还显示了准确的度量姿势估计性能 - 估计中位误差为10 cm和0.33度的6 -DOF姿势。
This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4% recall at 100% precision where the next closest competitor follows with 64.9%. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10 cm and 0.33 deg.
