3 citations · 9 across the 10 of their papers we have counts for
10 papers
A Distributed Multi-UGV Exploration Framework With Loop-Aware Planning and Descriptor-Aided Localization in Resource-Limited Environments
Zhiwei Li, Haiou Liu, Xijun Zhao +3
Robust and efficient cooperative exploration with multiple unmanned ground vehicles (UGVs) in unknown, GPSdenied, and bandwidth-limited environments without prior maps remains chal…
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection
Ruoyu Xu, Zhiyu Xiang, Chenwei Zhang +6
3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor i…
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
Junbao Zhou, Jilin Mei, Pengze Wu +4
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem…
RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments
Qihao Peng, Zhiyu Xiang, YuanGang Fan +2
As a fundamental task for intelligent robots, visual SLAM has made great progress over the past decades. However, robust SLAM under highly weak-textured environments still remains…
CVFNet: Real-time 3D Object Detection by Learning Cross View Features
Jiaqi Gu, Zhiyu Xiang, Pan Zhao +4
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies. Although voxel or point based methods a…
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Biao Gao, Xijun Zhao, Huijing Zhao
Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-…
An Image-based Approach of Task-driven Driving Scene Categorization
Shaochi Hu, Hanwei Fan, Biao Gao +2
Categorizing driving scenes via visual perception is a key technology for safe driving and the downstream tasks of autonomous vehicles. Traditional methods infer scene category by…
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
Biao Gao, Shaochi Hu, Xijun Zhao +1
Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as…
Off-Road Drivable Area Extraction Using 3D LiDAR Data
Biao Gao, Anran Xu, Yancheng Pan +3
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to dea…
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
Jilin Mei, Biao Gao, Donghao Xu +3
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including…