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The high cost of planetary rover missions limits risk-taking and therefore restricts scientific exploration. Also, limited autonomy requires time-consuming manual commands that must be issued to the rover from a great distance. This paper explores the combination of vision-based geological information inferred from a Bayesian Network (BN) with the guidance(More)
Without an absolute position sensor (e.g., GPS), an accurate heading estimate is necessary for proper localization of an autonomous unmanned vehicle or robot. This paper introduces direction maps (DMs), which represent the directions of only dominant surfaces of the vehicle’s environment and can be created with negligible effort. Given an environment with(More)
This paper introduces two-dimensional axis mapping, which estimates axis maps (AMs) based on LiDAR measurements. An AM describes the dominant orientations of surfaces in an environment and is void of positional information. As a consequence of the directional nature of the map, there are significant differences compared with traditional mapping algorithms.(More)
An axis map (AM) represents the orientations of planar surfaces in an environment and is void of positional information. Three-dimensional axis mapping (3DAM) is a graph-based optimization algorithm that generates an AM, while carefully addressing the parameterization of axes (i.e., normal vectors). 3DAM exploits the lack of positional information to form a(More)
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