3,820 research outputs found
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Abstract-Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-ofthe-art performance of the proposed method
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
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
To assure that an autonomous car is driving safely on public roads, its
object detection module should not only work correctly, but show its prediction
confidence as well. Previous object detectors driven by deep learning do not
explicitly model uncertainties in the neural network. We tackle with this
problem by presenting practical methods to capture uncertainties in a 3D
vehicle detector for Lidar point clouds. The proposed probabilistic detector
represents reliable epistemic uncertainty and aleatoric uncertainty in
classification and localization tasks. Experimental results show that the
epistemic uncertainty is related to the detection accuracy, whereas the
aleatoric uncertainty is influenced by vehicle distance and occlusion. The
results also show that we can improve the detection performance by 1%-5% by
modeling the aleatoric uncertainty.Comment: Accepted to present in the 21st IEEE International Conference on
Intelligent Transportation Systems (ITSC 2018
PIXOR: Real-time 3D Object Detection from Point Clouds
We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data more efficiently by representing the scene from the Bird's Eye View
(BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs
oriented 3D object estimates decoded from pixel-wise neural network
predictions. The input representation, network architecture, and model
optimization are especially designed to balance high accuracy and real-time
efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection
benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets
we show that the proposed detector surpasses other state-of-the-art methods
notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add
acknowledgemen
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