Hybrid Forest Mapping for Robotic Navigation under GNSS Uncertainty

Abstract

This paper examines the problem of constructing a navigation-oriented spatial model of a forested area under conditions of limited satellite navigation accuracy. In dense forest stands, GNSS observations are subject to significant errors, which distorts the spatial structure and reduces the distinguishability of objects. A hybrid method is proposed based on the integration of global coordinates obtained using GNSS devices and local geometric measurements of distances and directions. The method incorporates polar measurements relative to control points, as well as inter-object distances, forming a graph model of the spatial structure. Coordinate reconstruction is performed within the framework of a weighted optimization problem, taking into account the heterogeneity of the initial data accuracy. Additionally, the model incorporates contextual environmental factors, including undergrowth, windfall zones, and topography, which affect the observability of objects and the quality of measurements. This improves the applicability of the method in real-world operating conditions of forestry machines and robotic systems. The proposed approach is shown to restore a geometrically consistent site structure, improve object distinguishability, and be robust to GNSS observation errors. The method is implemented using readily available measurement tools and can be applied to both field mapping and forestry machine automation systems.