Slam algorithm for multi-robot communication in unknown environment based on particle filter

To solve the SLAM (simultaneous localization and map building) of mobile robots, a multi-robot SLAM algorithm based on particle filter for communication in unknown environment is proposed. In the standard particle filter, the incremental map construction method based on point-line consistency is introduced to preserve the hypothesis of the line segment feature map in each particle, and the importance function is introduced to the observation information. On this basis, sampling map fusion algorithm extracts ORB (oriented FAST and rotated BRIEF) features of local map constructed by a single robot and finds the optimal matching points, calculates the homography matrix of local map optimal matching point set, determines the optimal affine transformation parameters among point sets, so as to complete the local map fusion, and realize the multi-robot communication based on SLAM in unknown environment. The results show that the algorithm achieves high-precision map fusion and it is an effective SLAM construction algorithm for multiple machines.