Abstract
Simultaneous Localization and mapping (SLAM) in robotics is a significant issue undergoing comprehensive investigation. Its involvement towards self-driving robot have drawn scientist's attention on SLAM. Previously, numerous methods are proposed to address the issue of SLAM with remarkable achievements but there are several factors having the capability to degrade the effectiveness of SLAM technique. These factors include environmental noises (light intensity and shadow), dynamic landmarks, kidnap robot and computational cost. These problems create misinterpretation with an erroneous effect in mapping/localization. In the attempt of addressing these problems, a novel SLAM technique Known as DIK-SLAM functioning on several modifications of Monte Carlo algorithm will be developed to increase its robustness while taking into consideration the computational complexity. Experiment will be carried out using Matlab for simulation and will be evaluated using a quantitative and qualitative method. Experimental results obtained will be compare with the original Monte Carlo algorithm. Successful implementation of the novel SLAM algorithm will lead to enhancement in robot trajectory and in real life will facilitate autonomous navigation, path planning and exploration while it reduces accident rates and injuries.
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