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

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

CNRS — Université de Toulouse

The unification problem in a logical system L can be defined in the following way: given a formula φ(x1,..., xα), determine whether there exists formulas ψ1,..., ψα such that φ(ψ1,..., ψα) is in L. The research on unification for modal logics was originally motivated by the admissibility problem for rules of inference: given a rul

Definability and canonicity for Boolean logic with a binary relation

International audienceThis paper studies the concepts of definability and canonicity in Boolean logic with a binary relation. Firstly, it provides formulas defining first-order or second-order conditions on frames. Secondly, it proves that all formulas corresponding to compatible first-order conditions on frames are canonical