RGB-D SLAM in Dynamic Environments Using Point Correlations
RGB-D SLAM in Dynamic Environments Using Point Correlations
复制标题
动态环境中使用点相关性的 RGB-D SLAM
DOI：
10.1109/tpami.2020.3010942
复制
发表时间：
2022-01-01
影响因子：
23.6
通讯作者：
Scherer, Sebastian
中科院分区：
文献类型：
--
作者：
Dai, Weichen;Zhang, Yu;Scherer, Sebastian
本文提出了一种在动态环境中消除运动目标影响的同时定位与地图构建（SLAM）方法。该方法利用地图点之间的相关性将静态场景的一部分的点和不同移动对象的一部分的点分成不同的组。首先使用Delaunay三角剖分从所有地图点创建稀疏图。在此图中，顶点表示地图点，每条边表示相邻点之间的相关性。如果两点之间的相对位置随时间保持一致，则它们之间存在相关性，并且它们被认为是刚性地一起移动。如果没有，则认为它们没有相关性，并将其分为不同的组。在点相关性优化期间去除不相关点之间的边缘之后，剩余的图将移动对象的映射点与静态场景的映射点分开。假设最大的组是可靠的静态映射点的组。最后，仅使用这些点来执行运动估计。所提出的方法被实施的RGB-D传感器，评估与公共RGB-D基准，并在几个额外的挑战性环境中进行测试。实验结果表明，所提出的SLAM方法可以实现在轻微和高度动态环境中的鲁棒性和准确的性能。与现有方法相比，该方法具有较高的精度和较好的实时性。
In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created using Delaunay triangulation from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed during point-correlation optimization, the remaining graph separates the map points of the moving objects from the map points of the static scene. The largest group is assumed to be the group of reliable static map points. Finally, motion estimation is performed using only these points. The proposed method was implemented for RGB-D sensors, evaluated with a public RGB-D benchmark, and tested in several additional challenging environments. The experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared with other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.