Fault-tolerant 3D Mapping with Application to an Orchard Robot

Abstract In this paper we present a geometric reasoning method for dealing with noise as well as faults present in 3D depth maps. These maps are acquired using stereo-vision sensors, but our framework makes no assumption about the origin of the underlying data. The method is based on observations made on the environment from different camera poses (viewpoints), where the occupied space as well as uncertainties in the range measurement are modelled using dynamic octree structures. This scheme allows us to detect and diagnose faulty range measurements in an efficient manner. We present results on the acquisition of comprehensive 3D maps for an agricultural robot operating in an orchard.

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