82 citations · 200 across the 17 of their papers we have counts for
17 papers
CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
Yecheng Lyu, Xinming Huang, Ziming Zhang
LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise…
LiDAR Odometry Methodologies for Autonomous Driving: A Survey
Nikhil Jonnavithula, Yecheng Lyu, Ziming Zhang
Vehicle odometry is an essential component of an automated driving system as it computes the vehicle's position and orientation. The odometry module has a higher demand and impact…
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementat…
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
Yecheng Lyu, Xinming Huang, Ziming Zhang
Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attra…
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Ce Zheng, Yecheng Lyu, Ming Li +1
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR fra…
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
Yecheng Lyu, Ming Li, Xinming Huang +3
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
Lin Bai, Yecheng Lyu, Xinming Huang
In recent years, convolutional neural network has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, often m…
Automatic Building and Labeling of HD Maps with Deep Learning
Mahdi Elhousni, Yecheng Lyu, Ziming Zhang +1
In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and…
A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN
Lin Bai, Yecheng Lyu, Xinming Huang
In this paper, a scalable neural network hardware architecture for image segmentation is proposed. By sharing the same computing resources, both convolution and deconvolution opera…
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
Lin Bai, Yecheng Lyu, Xin Xu +1
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform f…
Learning to Segment 3D Point Clouds in 2D Image Space
Yecheng Lyu, Xinming Huang, Ziming Zhang
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 a…
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids. To bridge the ga…
An Interactive LiDAR to Camera Calibration
Yecheng Lyu, Lin Bai, Mahdi Elhousni +1
Recent progress in the automated driving system (ADS) and advanced driver assistant system (ADAS) has shown that the combined use of 3D light detection and ranging (LiDAR) and the…
Road Segmentation Using CNN and Distributed LSTM
Yecheng Lyu, Lin Bai, Xinming Huang
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…
ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
Yecheng Lyu, Lin Bai, Xinming Huang
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 rangi…
Road Segmentation Using CNN with GRU
Yecheng Lyu, Xinming Huang
This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU). For autonomous vehicles, road seg…
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA
Yecheng Lyu, Lin Bai, Xinming Huang
This paper presents the FPGA design of a convolutional neural network (CNN) based road segmentation algorithm for real-time processing of LiDAR data. For autonomous vehicles, it is…