22,294 research outputs found
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
Cone Detection using a Combination of LiDAR and Vision-based Machine Learning
The classification and the position estimation of objects become more and
more relevant as the field of robotics is expanding in diverse areas of
society. In this Bachelor Thesis, we developed a cone detection algorithm for
an autonomous car using a LiDAR sensor and a colour camera. By evaluating
simple constraints, the LiDAR detection algorithm preselects cone candidates in
the 3 dimensional space. The candidates are projected into the image plane of
the colour camera and an image candidate is cropped out. A convolutional neural
networks classifies the image candidates as cone or not a cone. With the fusion
of the precise position estimation of the LiDAR sensor and the high
classification accuracy of a neural network, a reliable cone detection
algorithm was implemented. Furthermore, a path planning algorithm generates a
path around the detected cones. The final system detects cones even at higher
velocity and has the potential to drive fully autonomous around the cones
Deep Multi-Sensor Lane Detection
Reliable and accurate lane detection has been a long-standing problem in the
field of autonomous driving. In recent years, many approaches have been
developed that use images (or videos) as input and reason in image space. In
this paper we argue that accurate image estimates do not translate to precise
3D lane boundaries, which are the input required by modern motion planning
algorithms. To address this issue, we propose a novel deep neural network that
takes advantage of both LiDAR and camera sensors and produces very accurate
estimates directly in 3D space. We demonstrate the performance of our approach
on both highways and in cities, and show very accurate estimates in complex
scenarios such as heavy traffic (which produces occlusion), fork, merges and
intersections.Comment: IEEE International Conference on Intelligent Robots and Systems
(IROS) 201
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
User assisted and automatic inverse procedural road modelling at the city scale
Cities are structured by roads. Having up to date and detailed maps of these
is thus an important challenge for urban planing, civil engineering and
transportation. Those maps are traditionally created manually, which represents
a massive amount of work, and may discard recent or temporary changes. For
these reasons, automated map building has been a long time goal, either for
road network reconstruction or for local road surface reconstruction from low
level observations. In this work, we navigate between these two goals. Starting
from an approximate road axis (+ width) network as a simple road modelling, we
propose to use observations of street features and optimisation to improve the
coarse model. Observations are generic, and as such, can be derived from
various data, such as aerial images, street images and street Lidar, other GIS
data, and complementary user input.
Starting from an initial road modelling which is at a median distance of 1.5
metre from the sidewalk ground-truth, our method has the potential to robustly
optimise the road modelling so the median distance reaches 0.45 metre fully
automatically, with better results possible using user inputs and/or more
precise observations. The robust non linear least square optimisation used is
extremely fast, with computing time from few minutes (whole Paris) to less than
a second for a few blocks.
The proposed approach is simple, very fast and produces a credible road
model. These promising results open the way to various applications, such as
integration in an interactive framework, or integration in a more powerful
optimisation method, which would be able to further segment road network and
use more complex road model.Comment: Article extracted form PhD (chap5
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
Graph convolution network based approaches have been recently used to model
region-wise relationships in region-level prediction problems in urban
computing. Each relationship represents a kind of spatial dependency, like
region-wise distance or functional similarity. To incorporate multiple
relationships into spatial feature extraction, we define the problem as a
multi-modal machine learning problem on multi-graph convolution networks.
Leveraging the advantage of multi-modal machine learning, we propose to develop
modality interaction mechanisms for this problem, in order to reduce
generalization error by reinforcing the learning of multimodal coordinated
representations. In this work, we propose two interaction techniques for
handling features in lower layers and higher layers respectively. In lower
layers, we propose grouped GCN to combine the graph connectivity from different
modalities for more complete spatial feature extraction. In higher layers, we
adapt multi-linear relationship networks to GCN by exploring the dimension
transformation and freezing part of the covariance structure. The adapted
approach, called multi-linear relationship GCN, learns more generalized
features to overcome the train-test divergence induced by time shifting. We
evaluated our model on ridehailing demand forecasting problem using two
real-world datasets. The proposed technique outperforms state-of-the art
baselines in terms of prediction accuracy, training efficiency,
interpretability and model robustness
Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO
In this paper, we address the basic problem of recognizing moving objects in
video images using Visual Vocabulary model and Bag of Words and track our
object of interest in the subsequent video frames using species inspired PSO.
Initially, the shadow free images are obtained by background modelling followed
by foreground modeling to extract the blobs of our object of interest.
Subsequently, we train a cubic SVM with human body datasets in accordance with
our domain of interest for recognition and tracking. During training, using the
principle of Bag of Words we extract necessary features of certain domains and
objects for classification. Subsequently, matching these feature sets with
those of the extracted object blobs that are obtained by subtracting the shadow
free background from the foreground, we detect successfully our object of
interest from the test domain. The performance of the classification by cubic
SVM is satisfactorily represented by confusion matrix and ROC curve reflecting
the accuracy of each module. After classification, our object of interest is
tracked in the test domain using species inspired PSO. By combining the
adaptive learning tools with the efficient classification of description, we
achieve optimum accuracy in recognition of the moving objects. We evaluate our
algorithm benchmark datasets: iLIDS, VIVID, Walking2, Woman. Comparative
analysis of our algorithm against the existing state-of-the-art trackers shows
very satisfactory and competitive results
Intelligent manipulation technique for multi-branch robotic systems
New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Lane detection in driving scenes is an important module for autonomous
vehicles and advanced driver assistance systems. In recent years, many
sophisticated lane detection methods have been proposed. However, most methods
focus on detecting the lane from one single image, and often lead to
unsatisfactory performance in handling some extremely-bad situations such as
heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In
fact, lanes are continuous line structures on the road. Consequently, the lane
that cannot be accurately detected in one current frame may potentially be
inferred out by incorporating information of previous frames. To this end, we
investigate lane detection by using multiple frames of a continuous driving
scene, and propose a hybrid deep architecture by combining the convolutional
neural network (CNN) and the recurrent neural network (RNN). Specifically,
information of each frame is abstracted by a CNN block, and the CNN features of
multiple continuous frames, holding the property of time-series, are then fed
into the RNN block for feature learning and lane prediction. Extensive
experiments on two large-scale datasets demonstrate that, the proposed method
outperforms the competing methods in lane detection, especially in handling
difficult situations.Comment: IEEE Transactions on Vehicular Technology, 69(1), 202
Automatic detection of passable roads after floods in remote sensed and social media data
This paper addresses the problem of floods classification and floods
aftermath detection utilizing both social media and satellite imagery.
Automatic detection of disasters such as floods is still a very challenging
task. The focus lies on identifying passable routes or roads during floods. Two
novel solutions are presented, which were developed for two corresponding tasks
at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification
of images providing evidence for road passability and (ii) differentiation and
detection of passable and non-passable roads in images from two complementary
sources of information. For the first challenge, we mainly rely on object and
scene-level features extracted through multiple deep models pre-trained on the
ImageNet and Places datasets. The object and scene-level features are then
combined using early, late and double fusion techniques. To identify whether or
not it is possible for a vehicle to pass a road in satellite images, we rely on
Convolutional Neural Networks and a transfer learning-based classification
approach. The evaluation of the proposed methods are carried out on the
large-scale datasets provided for the benchmark competition. The results
demonstrate significant improvement in the performance over the recent
state-of-art approaches
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