5,518 research outputs found
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Perception technologies in Autonomous Driving are experiencing their golden
age due to the advances in Deep Learning. Yet, most of these systems rely on
the semantically rich information of RGB images. Deep Learning solutions
applied to the data of other sensors typically mounted on autonomous cars (e.g.
lidars or radars) are not explored much. In this paper we propose a novel
solution to understand the dynamics of moving vehicles of the scene from only
lidar information. The main challenge of this problem stems from the fact that
we need to disambiguate the proprio-motion of the 'observer' vehicle from that
of the external 'observed' vehicles. For this purpose, we devise a CNN
architecture which at testing time is fed with pairs of consecutive lidar
scans. However, in order to properly learn the parameters of this network,
during training we introduce a series of so-called pretext tasks which also
leverage on image data. These tasks include semantic information about
vehicleness and a novel lidar-flow feature which combines standard image-based
optical flow with lidar scans. We obtain very promising results and show that
including distilled image information only during training, allows improving
the inference results of the network at test time, even when image data is no
longer used.Comment: Presented in IEEE ICRA 2018. IEEE Copyrights: Personal use of this
material is permitted. Permission from IEEE must be obtained for all other
uses. (V2 just corrected comments on arxiv submission
Deep Semantic Classification for 3D LiDAR Data
Robots are expected to operate autonomously in dynamic environments.
Understanding the underlying dynamic characteristics of objects is a key
enabler for achieving this goal. In this paper, we propose a method for
pointwise semantic classification of 3D LiDAR data into three classes:
non-movable, movable and dynamic. We concentrate on understanding these
specific semantics because they characterize important information required for
an autonomous system. Non-movable points in the scene belong to unchanging
segments of the environment, whereas the remaining classes corresponds to the
changing parts of the scene. The difference between the movable and dynamic
class is their motion state. The dynamic points can be perceived as moving,
whereas movable objects can move, but are perceived as static. To learn the
distinction between movable and non-movable points in the environment, we
introduce an approach based on deep neural network and for detecting the
dynamic points, we estimate pointwise motion. We propose a Bayes filter
framework for combining the learned semantic cues with the motion cues to infer
the required semantic classification. In extensive experiments, we compare our
approach with other methods on a standard benchmark dataset and report
competitive results in comparison to the existing state-of-the-art.
Furthermore, we show an improvement in the classification of points by
combining the semantic cues retrieved from the neural network with the motion
cues.Comment: 8 pages to be published in IROS 201
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point
clouds. We propose an encoding of sparse 3D data from the Velodyne sensor
suitable for training a convolutional neural network (CNN). This general
purpose approach is used for segmentation of the sparse point cloud into ground
and non-ground points. The LiDAR data are represented as a multi-channel 2D
signal where the horizontal axis corresponds to the rotation angle and the
vertical axis the indexes channels (i.e. laser beams). Multiple topologies of
relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and
evaluated using a manually annotated dataset we prepared. The results show
significant improvement of performance over the state-of-the-art method by
Zhang et al. in terms of speed and also minor improvements in terms of
accuracy.Comment: ICRA 2018 submissio
Hallucinating dense optical flow from sparse lidar for autonomous vehicles
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacement of any image-based optical flow system when images are not reliable due to e.g. adverse weather conditions or at night. In order to infer high resolution 2D flows from discrete range data we devise a three-block architecture of multiscale filters that combines multiple intermediate objectives, both in the lidar and image domain. To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2. We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with those estimated with image-based methods.Peer ReviewedPostprint (author's final draft
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