Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments

Abstract

Spatial awareness and memory are key factors for a robot to evolve in semi-structured and dynamic environments as those found in agriculture, and particularly in fruit crops where the trees are regularly distributed. This paper proposes a probabilistic method for mapping out-of-structure objects (weeds, workers, machines, fallen branches, etc.) using a Kernel density estimator. The methodology has theoretical and practical advantages over the well-known occupancy grid map estimator such as optimization of storage resources, online update, high resolution, and straightforward adaptability to dynamic environments. An example application would be a control scheme through which a robot is able to perform cautious navigation in areas with high probability of finding obstacles. Simulations and experiments show that large extensions can be online mapped with few data and high spatial resolution.