paper

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

arXiv:1902.10194 · doi:10.1109/LRA.2019.2895264

Abstract

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.

Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localization

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