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

Can’t see the wood for the trees? How do learner attitudes affect idiom usage?

departmental bulletin pape

Relative Resistance to Breaking of Pinus taeda L. and Pinus palustris

Patterns from hurricane damage give an indication that longleaf pine (Pinus palustris) is more windfirm than loblolly pine (Pinus taeda). Tree windfirmess has been attributed to many factors including species and material properties like wood strength and stiffness. Because longleaf pine wood is stronger and stiffer than loblolly pine wood, this study used static winching methodology to see if these properties account for differences in windfirmness by measuring bending force required to break stems (MMAX). Stress-strain diagrams were constructed for pulled trees to explore how they behave under increasing loads. Based on these diagrams, it appears that living trees can act as linear elastic materials as they experience increasing static lateral stress. As expected, longleaf pine stems were stiffer than loblolly pine wood in situ based on Young’s modulus of elasticity derived from these diagrams. Tree basal area was the best predictor of MMAX for both species, however, species had no effect on the maximum bending moment required to break tree stems of a given basal area for these trees under these conditions. The stiffness of the stems was higher for longleaf than loblolly as indicated by the modulus of elasticity, but the strength of the stems as indicated by the modulus of rupture was not significantly different between the species