40 research outputs found
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
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
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a
transformed LiDAR point cloud as input and directly outputs a point-wise label
map, which is then refined by a conditional random field (CRF) implemented as a
recurrent layer. Instance-level labels are then obtained by conventional
clustering algorithms. Our CNN model is trained on LiDAR point clouds from the
KITTI dataset, and our point-wise segmentation labels are derived from 3D
bounding boxes from KITTI. To obtain extra training data, we built a LiDAR
simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize
large amounts of realistic training data. Our experiments show that SqueezeSeg
achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per
frame), highly desirable for autonomous driving applications. Furthermore,
additionally training on synthesized data boosts validation accuracy on
real-world data. Our source code and synthesized data will be open-sourced
SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
In this paper, we introduce a deep encoder-decoder network, named SalsaNet,
for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments
the road, i.e. drivable free-space, and vehicles in the scene by employing the
Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack
of annotated point cloud data, in particular for the road segments, we
introduce an auto-labeling process which transfers automatically generated
labels from the camera to LiDAR. We also explore the role of imagelike
projection of LiDAR data in semantic segmentation by comparing BEV with
spherical-front-view projection and show that SalsaNet is projection-agnostic.
We perform quantitative and qualitative evaluations on the KITTI dataset, which
demonstrate that the proposed SalsaNet outperforms other state-of-the-art
semantic segmentation networks in terms of accuracy and computation time. Our
code and data are publicly available at
https://gitlab.com/aksoyeren/salsanet.git