Adaptive Scan-Correlation for Mobile Robot Localization in Unstructured Environments Open Access
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Mobile robot localization is fundamental to the development of more proficient robots capable of operating in complex, unstructured environments. However, many environments in which mobile robots are operating may be devoid of the static landmark and/or lack geometric primitives required for feature-based localization techniques. For such environments, scan--correlation have been employed. These approaches rely on the temporal correlation of unprocessed data to measure the relative displacement, i.e. motion, between successive scans obtained from a laser range finder. This research provides a comprehensive analysis of an adaptive scan--correlation technique that leverages previous effort to address real-time computational constraints and data association issues for mobile robots localization in complex, unstructured environments. This analysis uses a two-pronged approach to identifying and testing performance singularities to identify errors a specific system is prone to, how these errors impact the overall performance of that system, and how performance of that system compares with competing approaches. This analysis will will lead to the discovering of three testing scenarios for characterizing the performance of mobile robot localization.