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This paper presents ORB-SLAM2S, a fast and complete simultaneous localization and mapping (SLAM) system based on ORB-SLAM2 for monocular, stereo, and RGB-D cameras. The system works, ensuring accuracy simultaneously, in real-time on standard central processing units (CPU) at a faster speed in small and large indoor and outdoor environments. The system includes a lightweight front-end which is a sparse optical flow method for non-keyframes to avoid the extraction of keypoints and descriptors that allows for high-speed real-time performance. For keyframes, a feature-based method is used to ensure the accurate trajectory estimation almost the same as ORB-SLAM2. The evaluation of famous public sequences shows that our method achieves almost the same state-of-the-art accuracy as ORB-SLAM2 and faster speed performance which is 3~5 times that of ORB-SLAM2, being in most cases the faster SLAM solution. As proved by experiments, the system provides a fast and lightweight visual SLAM while ensuring accuracy for low-cost mobile devices.

The artificial ecological pyramid model is proposed to solve complex problems, which is a layered evolutionary multi-agent system model. The model has three layers. The lowest layer is composed of "stimulation-response" agents called base agents. The second layer is composed of combined agents that are combinations of base agents. The top layer is composed of advanced agents that have reasoning capabilities. Agents in all layers must be up against the test of environment. Agents on the lowest layer can only improve their fitness evaluations by evolutional methods, and agents on upper layers can also improve their fitness evaluations by "eating" agents on lower layers. We also construct a strategy system based on artificial ecological pyramid model to solve the problem of rivalry between autonomous robots. The simulation results of the test on soccer robot system prove the model effect