A sliding window based feature extraction method with its application to 3D grid map fusion As the scale of the environment increases, the single robot simultaneous localization and mapping (SLAM) is not the best choice for building maps. In recent years, more and more researchers have focused on multirobot SLAM. Multirobot SLAM has the advantages of high efficiency, high precision of mapping and strong fault tolerance. However, multirobot SLAM needs to solve the problem of map fusion. To solve this problem, this paper proposes an algorithm based on the maximum common subgraph for 3D grid map fusion, which does not require the pose relationship between robots. According to the characteristics of the indoor environment, the points where the three faces intersect are defined as corner points. Firstly, this paper proposes a sliding windowbased feature point extraction method for 3D grid map. Moving the window to determine whether the point is a flat point, an edge point or a corner point according to the change in the number of occupied cubes in the window. Then we fit a plane to reject points that are not intersecting by three faces. Secondly, the backtracking method is used to find the maximum common subgraph. Finally, it calculates the transformation matrix according to the matched feature points to realize the fusion of 3D grid maps. The experimental results show that the proposed method can accurately fuse the 3D grid map with rotations.

A Triangle Feature Based Maptomap Matching and Loop Closure for 2D Graph SLAM The loop closure problem in 2D LiDAR simultaneous localization and mapping (SLAM) is interesting yet to be solved efficiently, as it suffers from a lack of information in 2D laser rangefinder readings. For this reason, we compare two grid submaps, where features and constraints are enriched, to find loops. We propose a geometric environment descriptor called a Triangle Feature (TF). It exploits the Euclidean distance constraint any three feature points in a submap can form. A 2D graph SLAM system using TF to close loops is also developed. The global maps we build outperform some popular opensource SLAM solutions, and the system can run up to 2.4 times of realtime in our experiments.