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.
Highlights
Mapping is the first step in the navigation work of mobile robots
The map merging method based on suppositional box is mainly divided into three steps: suppositional box construction, map matching based on suppositional box, and map merging
We propose a map merging method based on suppositional box
Summary
Mapping is the first step in the navigation work of mobile robots. Some tasks of the robot, such as ruin detection or indoor rescue, require high efficiency and quality in the process of mapping. One part of method with known initial pose is to extend the single-robot SLAM (Simultaneous Localization And Mapping) method to a multi-robot system, and the other part is to combine optimization algorithms to reduce the cumulative error. In this method, an important challenge is that the estimation of initial pose is complicated and has certain errors. PF-SLAM (Particle Filter SLAM) is extended to multi-robots [4] This method is discussed separately by assuming whether the initial position is known. Sasaoka et al [5] studied multi-robot slam based on information fusion and EKF (Extended Kalman Filter)
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