Review and analysis of optical sensor-based technical vision systems technologies for autonomous navigation on dirt roads

This review is devoted to computer vision technologies for the autonomous navigation of a mobile robot on dirt roads, and to analyzing their degree of technological readiness. The selection of studies was conducted according to the PRISMA methodology using the academic article aggregator Google Scholar. Based on the analysis of the works from the selected sample, key technologies were identified, including datasets, terrain mapping techniques, and methods for road and obstacle detection. These were further divided into sub-technologies, each of which was evaluated for its level of technological readiness using the scale presented in the study – a newly proposed interpretation of the TRL scale – taking into account the particular challenges of working on dirt roads and in environments that are generally difficult to replicate under laboratory conditions. As a result of the study, statistics were compiled that highlight the most significant works in the field of autonomous navigation on dirt roads. It was also concluded that the main trend in navigation development involves the acquisition and processing of comprehensive data, that traversability analysis focuses on the extraction and processing of geometric features, and that there is an urgent need for high-quality datasets.

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