1,420 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
Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds
Despite the good results that have been achieved in unimodal segmentation,
the inherent limitations of individual data increase the difficulty of
achieving breakthroughs in performance. For that reason, multi-modal learning
is increasingly being explored within the field of remote sensing. The present
multi-modal methods usually map high-dimensional features to low-dimensional
spaces as a preprocess before feature extraction to address the nonnegligible
domain gap, which inevitably leads to information loss. To address this issue,
in this paper we present our novel Imbalance Knowledge-Driven Multi-modal
Network (IKD-Net) to extract features from raw multi-modal heterogeneous data
directly. IKD-Net is capable of mining imbalance information across modalities
while utilizing a strong modal to drive the feature map refinement of the
weaker ones in the global and categorical perspectives by way of two
sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and
Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized
using a holistic loss function. While we were developing IKD-Net, we also
established a new dataset called the National Agriculture Imagery Program and
3D Elevation Program Combined dataset in California (N3C-California), which
provides a particular benchmark for multi-modal joint segmentation tasks. In
our experiments, IKD-Net outperformed the benchmarks and state-of-the-art
methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset.
IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC
2018 challenge evaluation until this paper's submission
Automated Structural-level Alignment of Multi-view TLS and ALS Point Clouds in Forestry
Access to highly detailed models of heterogeneous forests from the near
surface to above the tree canopy at varying scales is of increasing demand as
it enables more advanced computational tools for analysis, planning, and
ecosystem management. LiDAR sensors available through different scanning
platforms including terrestrial, mobile and aerial have become established as
one of the primary technologies for forest mapping due to their inherited
capability to collect direct, precise and rapid 3D information of a scene.
However, their scalability to large forest areas is highly dependent upon use
of effective and efficient methods of co-registration of multiple scan sources.
Surprisingly, work in forestry in GPS denied areas has mostly resorted to
methods of co-registration that use reference based targets (e.g., reflective,
marked trees), a process far from scalable in practice. In this work, we
propose an effective, targetless and fully automatic method based on an
incremental co-registration strategy matching and grouping points according to
levels of structural complexity. Empirical evidence shows the method's
effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety
of ecosystem conditions including pre/post fire treatment effects, of interest
to forest inventory surveyors
Monocular 3D Scene Reconstruction for an Autonomous Unmanned Aerial Vehicle
Rekonstrukce 3D modelu prostředí je klíčovou částí autonomního letu bezpilotní helikoptéry (UAV). Kombinace inerciální měřicí jednotky (IMU) a kamery je běžnou a dostupnou senzorovou sadou, jež je schopna získat informaci o měřítku prostředí. Tato práce si klade za cíl vyvinout algoritmus řešící problém 3D rekostrukce pro tyto senzory za využití existujících metod vizuálně-inerciální lokalizace (VINS). V práci jsou navrženy dva algoritmy, odlišené způsobem, jakým extrahují korespondence mezi snímky: párovací algoritmus se širokou bází a algoritmus založený na trackingu s malou bází. Také je implementována metoda vylepšující výslednou 3D strukturu po letu. Algoritmy jsou otestovány na veřejně dostupné datové sadě. Navíc jsou otestovány v simulátoru a je proveden experiment v reálném prostředí. Výsledky ukazují, že algoritmus založený na trackingu dosahuje výrazně lepších výsledků. Navíc testy na datech a experimenty v reálném prostředí ukazují, že algoritmus může být nasazen v praktických aplikačních situacích.The real-time 3D reconstruction of the surrounding scene is a key part in the pipeline of the autonomous flight of unmanned aerial vehicle (UAV). The combination of an inertial measurement unit (IMU) and a monocular camera is a common and inexpensive sensor setup that can be used to recover the scale of the environment. This thesis aims to develop an algorithm solving this problem for this particular setup by leveraging the existing visual-inertial navigation system (VINS) odometry algorithms for localisation. Two algorithms are developed, wide-baseline matching-based and small-baseline tracking-based. Also, an offline post-processing structure-refinement step is implemented to further improve the resulting structure. The algorithms and the refinement step are then evaluated on publicly available datasets. Furthermore, they are tested in a simulator, and a real-world experiment is conducted. The results show that the tracking-based algorithm is significantly more performant. Importantly, tests on the datasets and the real-world experiments suggest that this algorithm can be practically employed in application scenarios
3-D Scene Reconstruction from Aerial Imagery
3-D scene reconstructions derived from Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques were analyzed to determine the optimal reconnaissance flight characteristics suitable for target reconstruction. In support of this goal, a preliminary study of a simple 3-D geometric object facilitated the analysis of convergence angles and number of camera frames within a controlled environment. Reconstruction accuracy measurements revealed at least 3 camera frames and a 6 convergence angle were required to achieve results reminiscent of the original structure. The central investigative effort sought the applicability of certain airborne reconnaissance flight profiles to reconstructing ground targets. The data sets included images collected within a synthetic 3-D urban environment along circular, linear and s-curve aerial flight profiles equipped with agile and non-agile sensors. S-curve and dynamically controlled linear flight paths provided superior results, whereas with sufficient data conditioning and combination of orthogonal flight paths, all flight paths produced quality reconstructions under a wide variety of operational considerations
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