3,086 research outputs found
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
3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel Map
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been
specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and
isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in
constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined
shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly
reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate
results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside
each object is performed based on the voxel’s geometric class. The work here evaluates the proposed algorithm in various
versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation
evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which
is novel.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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