Sajad Saeedi, Liam Paull, Michael Trentini, and Howard Li
Simultaneous localization and mapping (SLAM), multiple robots,map merging, segmentation, Radon transform, image entropy
In robotics, the key requirement for achieving autonomy is to provide robots with the ability to accurately map an environment and simultaneously localize themselves within that environment. This problem is referred to as simultaneous localization and mapping (SLAM). In this research, a decentralized platform for SLAM with multiple robots has been developed. Single-robot SLAM is achieved through extended Kalman filter (EKF) data fusion. This approach is then extended to multiple-robot SLAM with a novel occupancy grid map fusion algorithm. Map fusion is achieved through a multi-step process that includes image preprocessing, segmentation, cross correlation, approximating the relative transformation matrix, tuning of the transformation matrix, and finally verification of the result. Results are shown from tests performed in real-world environments with multiple homogeneous robotic platforms.1
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