Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
This addresses localization challenges for robots like drones or quadrupeds in natural settings, but it is incremental as it builds on existing deep learning methods for point cloud processing.
The paper tackles laser-based localization in urban and natural environments by proposing a deep learning approach that learns descriptors from 3D point clouds for loop closure detection, resulting in a small model deployable on a CPU for robots with limited computational payload.
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.