Information-fusion based robot simultaneous localization and mapping adapted to search and rescue cluttered environment

The information-fusion methods are developed in this paper for mobile robots performing simultaneous localization and mapping (SLAM) adapting search and rescue (SAR) environment. Fusion systems consist of laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. The integrated particle filter algorithms run through the proposed informationfusion systems to perform SLAM task in collapsed disaster scenarios. We discussed several fusion approaches which include parallel measurements filtering, exploration trajectories fusing, and combination sensors' measurements and mobile robots' trajectories. The different fusion errors are analyzed by comparing the estimated trajectories and fusion trajectory to true trajectories, respectively. The simulations and experiments validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.