论文标题
基于飞机DF的长期映射的更改检测
PlaneSDF-based Change Detection for Long-term Dense Mapping
论文作者
论文摘要
在多个会话中处理环境图的能力对于长时间运行的机器人至关重要。具体而言，自主代理人希望检测不同会话的地图之间的变化，以便对当前环境产生无冲突的理解。在本文中，我们研究了基于新地图表示的变化检测问题，称为平面签名距离场（PlanesDF），其中密集的地图表示为平面的集合及其SDF体积中的相关几何成分。给定的源场和目标场景的点云，我们提出了一种基于三步的平面变更检测方法：（1）平面卷卷在每个场景中实例化并使用平面姿势在场景中注册；通过高度投影和连接的组件分析提取2D高度图和对象图。 （2）比较高度图并与对象图相交，以生成源场景中更改对象候选的2D更改位置掩码。 （3）使用SDF衍生的每个对象候选者进行更改掩码细化的功能进行3D几何验证。我们在合成数据集和现实世界数据集上评估了我们的方法，并通过更改对象检测的任务来证明其有效性。补充视频：https：//youtu.be/oh-mqpwtwzi
The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environment. In this paper, we look into the problem of change detection based on a novel map representation, dubbed Plane Signed Distance Fields (PlaneSDF), where dense maps are represented as a collection of planes and their associated geometric components in SDF volumes. Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis. (2) Height maps are compared and intersected with the object map to produce a 2D change location mask for changed object candidates in the source scene. (3) 3D geometric validation is performed using SDF-derived features per object candidate for change mask refinement. We evaluate our approach on both synthetic and real-world datasets and demonstrate its effectiveness via the task of changed object detection. Supplementary video: https://youtu.be/oh-MQPWTwZI
