Mutually converted arc–line segment-based SLAM with summing parameters

This paper presents a mutually converted arc–line segment-based simultaneous localization and mapping (SLAM) algorithm by distinguishing what we call the summing parameters from other types. These redefined parameters are a combination of the coordinate values of the measuring points. Unlike most traditional features-based simultaneous localization and mapping algorithms that only update the same type of features with a covariance matrix, our algorithm can match and update different types of features, such as the arc and line. For each separated data set from every new scan, the necessary information of the measured points is stored by the small constant number of the summing parameters. The arc and line segments are extracted according to the different limit values but based on the same parameters, from which their covariance matrix can also be computed. If one stored segment matches a new extracted segment successfully, two segments can be merged as one whether the features are the same type or not. The mergence is achieved by only summing the corresponding summing parameters of the two segments. Three simultaneous localization and mapping experiments in three different indoor environments were done to demonstrate the robustness, accuracy, and effectiveness of the proposed method. The data set of the Massachusetts Institute Of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) Building was used to validate that our method has good adaptability.