2,946 research outputs found
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
A Solution for Multi-Alignment by Transformation Synchronisation
The alignment of a set of objects by means of transformations plays an
important role in computer vision. Whilst the case for only two objects can be
solved globally, when multiple objects are considered usually iterative methods
are used. In practice the iterative methods perform well if the relative
transformations between any pair of objects are free of noise. However, if only
noisy relative transformations are available (e.g. due to missing data or wrong
correspondences) the iterative methods may fail.
Based on the observation that the underlying noise-free transformations can
be retrieved from the null space of a matrix that can directly be obtained from
pairwise alignments, this paper presents a novel method for the synchronisation
of pairwise transformations such that they are transitively consistent.
Simulations demonstrate that for noisy transformations, a large proportion of
missing data and even for wrong correspondence assignments the method delivers
encouraging results.Comment: Accepted for CVPR 2015 (please cite CVPR version
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