154 citations · 712 across the 31 of their papers we have counts for
31 papers
SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing
Jianyuan Ruan, Bo Li, Yibo Wang +1
Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dens…
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are…
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about…
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network g…
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
M. Usman Maqbool Bhutta, Yuxiang Sun, Darwin Lau +1
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performan…
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important i…
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Yuxiang Sun, Bo Yuan, Yufan Xue +3
Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement l…
On Bundle Adjustment for Multiview PointCloud Registration
Huaiyang Huang, Yuxiang Sun, Jin Wu +5
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise…
Incorporating Learnt Local and Global Embeddings into Monocular Visual SLAM
Huaiyang Huang, Haoyang Ye, Yuxiang Sun +2
Traditional approaches for Visual Simultaneous Localization and Mapping (VSLAM) rely on low-level vision information for state estimation, such as handcrafted local features or the…
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should a…
CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images
Zhenhua Xu, Yuxiang Sun, Lujia Wang +1
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. M…
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
Hengli Wang, Peide Cai, Yuxiang Sun +2
Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretabili…
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
Hengli Wang, Peide Cai, Rui Fan +2
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, mo…
iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving
Zhenhua Xu, Yuxiang Sun, Ming Liu
Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are dete…
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs
Hengli Wang, Yuxiang Sun, Rui Fan +1
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based…
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms
Hengli Wang, Rui Fan, Yuxiang Sun +1
Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CN…
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
Zhenhua Xu, Yuxiang Sun, Ming Liu
Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online ro…
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
Xiaodong Mei, Yuxiang Sun, Yuying Chen +2
In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we pro…
Geometric Structure Aided Visual Inertial Localization
Huaiyang Huang, Haoyang Ye, Jianhao Jiao +2
Visual Localization is an essential component in autonomous navigation. Existing approaches are either based on the visual structure from SLAM/SfM or the geometric structure from d…
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to mainta…
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
Hengli Wang, Rui Fan, Yuxiang Sun +1
The joint detection of drivable areas and road anomalies is a crucial task for ground mobile robots. In recent years, many impressive semantic segmentation networks, which can be u…
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs
Hengli Wang, Yuxiang Sun, Ming Liu
The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentati…
GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models
Huaiyang Huang, Haoyang Ye, Yuxiang Sun +1
Incorporating prior structure information into the visual state estimation could generally improve the localization performance. In this letter, we aim to address the paradox betwe…
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
Peide Cai, Sukai Wang, Yuxiang Sun +1
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. R…