We are addressing a major problem in autonomous robotics, which is simultaneous localization and mapping (SLAM). Currently there are many methods available which to varying degrees solve the localization problem of SLAM. The majority use probabilistic concepts. However, these solutions often bring with them a high level of algorithmic complexity. This can raise the cost of implementation and make fundamental concepts difficult to comprehend. Furthermore, because high complexity may not be necessary when the constraints of time and accuracy are not as severe, some applications do not require the rigor of a full probabilistic SLAM method. A solution to the localization problem which focuses on clarity and first principles can be beneficial to the engineering/education community and for applications where low cost is favored over high performance. Therefore, we present a straightforward approach to the localization portion of the SLAM problem using scan matching techniques. By reducing complex scan matching from a multidimensional to a one-dimensional problem, localization can be solved in an easily understandable way while minimizing cost and computational requirements. The solution presented here provides estimates of the robot pose and orientation within the confines of a static and simple environment. The techniques utilized are, cross correlation calculations for pure rotation movements around the robot’s center axis combined with simple root mean squared error calculations for translation movements.

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