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Found 15 papers, 4 papers with code
CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames and apply CoFi to estimate the pose on an efficient point cloud map.
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation.
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
In recent years, point cloud representation in 2D space has attracted increasing research interest since it exposes the local geometry features in a 2D space.
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics.
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
General graphs are difficult for learning due to their irregular structures.
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper.
Automatic Building and Labeling of HD Maps with Deep Learning
This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.
A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN
In this paper, a scalable neural network hardware architecture for image segmentation is proposed.
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization.
Learning to Segment 3D Point Clouds in 2D Image Space
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids.
ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time.
Road Segmentation Using CNN and Distributed LSTM
In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning.
Road Segmentation Using CNN with GRU
This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU).
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA
In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data.
Robotics
