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This thesis provides techniques to address some outstanding problems in robotic navigation in relatively large environments using simultaneous localisation and mapping (SLAM), resulting in improved competence and reliability of autonomous agents. Autonomous mobile agents are helpful in a diverse range of applications deemed as dull, dirty or dangerous for human operators including mining, defence and underwater explorations. In order to be fully autonomous the robot is required to incrementally construct a map of its vicinity and simultaneously localise itself within the environment based only on its on-board sensory data. However, the robot is confronted with a number of serious challenges that impair large-scale navigation and could even render SLAM results intractable in real time. This work provides efficient strategies for addressing such problems. The techniques presented include an effective method of reducing computational burden of updating the covariance matrix at every step, and cutting down the storage requirements. In addition, the usually complex and tedious task of transforming and fusing sub-maps into a single global map is simplified. Furthermore, an intelligent SLAM (I -SLAM) is introduced that enables accurate place recognition and minimise pose estimation errors. It also provides the means of adaptively adjusting to the nature of natural surface terrains for both indoor and outdoor environments. These techniques promise an enormous potential for autonomous agents operating in unknown environments in terms of consistency, accuracy and efficiency. Simulation results demonstrate the reliability and effectiveness of the system as a means of addressing some of the outstanding challenges with regards to large-scale SLAM performance |
