no code implementations • 19 Oct 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
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.
no code implementations • 23 May 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation.
no code implementations • 3 Mar 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
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.
no code implementations • 1 Sep 2020 • Ce Zheng, Yecheng Lyu, Ming Li, Ziming Zhang
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics.
1 code implementation • 21 Jun 2020 • Yecheng Lyu, Ming Li, Xinming Huang, Ulkuhan Guler, Patrick Schaumont, Ziming Zhang
General graphs are difficult for learning due to their irregular structures.
1 code implementation • 13 Jun 2020 • Lin Bai, Yecheng Lyu, Xinming Huang
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.
no code implementations • 1 Jun 2020 • Mahdi Elhousni, Yecheng Lyu, Ziming Zhang, Xinming Huang
This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.
no code implementations • 29 May 2020 • Lin Bai, Yecheng Lyu, Xin Xu, Xinming Huang
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization.
no code implementations • 29 May 2020 • Lin Bai, Yecheng Lyu, Xinming Huang
In this paper, a scalable neural network hardware architecture for image segmentation is proposed.
1 code implementation • CVPR 2020 • 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 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.
no code implementations • 26 Sep 2019 • 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.
2 code implementations • 10 Aug 2018 • 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 map for path planning.
no code implementations • 10 Aug 2018 • 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 ranging (LiDAR) data in real-time.
no code implementations • 14 Apr 2018 • 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).
no code implementations • 7 Nov 2017 • Yecheng Lyu, Lin Bai, Xinming Huang
In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data.
Robotics