no code implementations • 7 Jul 2022 • Qihao Peng, Zhiyu Xiang, YuanGang Fan, Tengqi Zhao, Xijun Zhao
As a fundamental task for intelligent robots, visual SLAM has made great progress over the past decades.
no code implementations • 13 Mar 2022 • Jiaqi Gu, Zhiyu Xiang, Pan Zhao, Tingming Bai, Lingxuan Wang, Xijun Zhao, Zhiyuan Zhang
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies.
no code implementations • 18 Feb 2022 • 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-road vehicles with various mechanical properties.
no code implementations • 5 Mar 2021 • Biao Gao, Shaochi Hu, Xijun Zhao, Huijing Zhao
With a set of human-annotated anchor patches, a feature representation is learned to discriminate regions with different traversability, a method of fine-grained semantic segmentation and mapping is subsequently developed for off-road scene understanding.
no code implementations • 10 Mar 2020 • Biao Gao, Anran Xu, Yancheng Pan, Xijun Zhao, Wen Yao, Huijing Zhao
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application.
no code implementations • 3 Sep 2018 • Jilin Mei, Biao Gao, Donghao Xu, Wen Yao, Xijun Zhao, Huijing Zhao
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications.
Robotics