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Localization in challenging, natural environments such as forests orwoodlands is an important capability for many applications from guiding a robotnavigating along a forest trail to monitoring vegetation growth with handheldsensors. In this work we explore laser-based localization in both urban andnatural environments, which is suitable for online applications. We propose adeep learning approach capable of learning meaningful descriptors directly from3D point clouds by comparing triplets (anchor, positive and negative examples).The approach learns a feature space representation for a set of segmented pointclouds that are matched between a current and previous observations. Ourlearning method is tailored towards loop closure detection resulting in a smallmodel which can be deployed using only a CPU. The proposed learning methodwould allow the full pipeline to run on robots with limited computationalpayload such as drones, quadrupeds or UGVs.

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU