Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

Abstract

For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

A Rover Team Based 3D Map Building Using Low Cost 2D Laser Scanners

“…The labeled dimensions of the developed BIM have been verified through ground truths. In comparison to reported 2D map merging solution [18], the developed BIM results of presented tests have been found quite satisfactory. The end user can view and compare the established 3D map along with BIM using standard softwares applicable for surveying applications.…”

“…Some researchers proposed global map merging solution using probabilistic Gaussian process applied on the individual RBPF based feature maps [17]. A recent work on map merging solution has presented use of prominent features of vertical lines and right-angled points of suppositional box in a KF framework [18]. Multiple indoor mapping results have presented in the work.…”

Real-time fusion study of raster maps based on feature matching

“…The local map effect and accuracy built by the submap will directly affect the global map accuracy after map fusion, so the submap should be preprocessed. In terms of map registration, the current mainstream feature matching methods based on feature points [3] , typical scale-invariant feature transform, speeded up robust feature, and oriented FAST and rotated BRIEF [4] . The improved map fusion algorithm of Blanco [5] et al mainly obtains their transformation matrix by the corresponding feature points in the two sub-maps constructed, and the remaining ones are divided into feature maps and raster maps, and then the transformation matrix is obtained by the RANSAC algorithm, which has the disadvantage that the resulting transformation matrix is inaccurate.…”

Evaluation of Lidar Odometry and Mapping Based on Reference Laser Scanning

“…Collecting a map in the form of a point cloud can provide crucial information about the localization, which in LiDARbased SLAM system is estimated on the basis of comparing subsequent maps. Accurate maps not only help maintain the correct position during the movement but also can assist in the detection of manual robot relocation or enable map merging from robots working in swarms (Chen et al 2021). Another asset emerging from accurate mapping is that additional information on the objects in the surroundings can be obtained.…”