691 research outputs found
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
In this work, a deep learning approach has been developed to carry out road
detection by fusing LIDAR point clouds and camera images. An unstructured and
sparse point cloud is first projected onto the camera image plane and then
upsampled to obtain a set of dense 2D images encoding spatial information.
Several fully convolutional neural networks (FCNs) are then trained to carry
out road detection, either by using data from a single sensor, or by using
three fusion strategies: early, late, and the newly proposed cross fusion.
Whereas in the former two fusion approaches, the integration of multimodal
information is carried out at a predefined depth level, the cross fusion FCN is
designed to directly learn from data where to integrate information; this is
accomplished by using trainable cross connections between the LIDAR and the
camera processing branches.
To further highlight the benefits of using a multimodal system for road
detection, a data set consisting of visually challenging scenes was extracted
from driving sequences of the KITTI raw data set. It was then demonstrated
that, as expected, a purely camera-based FCN severely underperforms on this
data set. A multimodal system, on the other hand, is still able to provide high
accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI
road benchmark where it achieved excellent performance, with a MaxF score of
96.03%, ranking it among the top-performing approaches
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world driving sequences and that is trained using automatically generated
training examples. Several combinations of input data were tested in order to
assess the performance gain provided by specific information modalities. The
fully convolutional neural network trained using all the available sensors
together with driving directions achieved the best MaxF score of 88.13% when
considering a region of interest of 60x60 meters. By considering a smaller
region of interest, the agreement between predicted paths and ground-truth
increased to 92.60%. The positive results obtained in this work indicate that
the proposed system may help fill the gap between low-level scene parsing and
behavior-reflex approaches by generating outputs that are close to vehicle
control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation
Using Fully Convolutional Neural Networks
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
In this work, a deep learning approach has been developed to carry out road
detection using only LIDAR data. Starting from an unstructured point cloud,
top-view images encoding several basic statistics such as mean elevation and
density are generated. By considering a top-view representation, road detection
is reduced to a single-scale problem that can be addressed with a simple and
fast fully convolutional neural network (FCN). The FCN is specifically designed
for the task of pixel-wise semantic segmentation by combining a large receptive
field with high-resolution feature maps. The proposed system achieved excellent
performance and it is among the top-performing algorithms on the KITTI road
benchmark. Its fast inference makes it particularly suitable for real-time
applications
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