The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing\nsimultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion\napproaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors� measurements and mobile\nrobots� trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are\nderived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists\nof multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars,\ngyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is\ncompared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real\nobservation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment\nand outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information fusion\nmethods in improving SLAM performances adapting to SAR scenarios.
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