most citedSalient Sparse Visual Odometry With Pose-Only Supervision

16 citations · 23 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV202416 cited

Salient Sparse Visual Odometry With Pose-Only Supervision

Siyu Chen, Kangcheng Liu, Chen Wang +3

Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods exce…

cs.RO2024

Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion''

Yingbing Chen, Jie Cheng, Sheng Wang +15

The rapid evolution of autonomous vehicles (AVs) has significantly influenced global transportation systems. In this context, we present ``Snow Lion'', an autonomous shuttle meticu…

cs.RO2023

Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

Pengyu Yin, Haozhi Cao, Thien-Minh Nguyen +4

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization p…

cs.CV20231 cited

3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

Aoran Xiao, Jiaxing Huang, Weihao Xuan +6

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) mode…

cs.RO2023

DoubleBee: A Hybrid Aerial-Ground Robot with Two Active Wheels

Muqing Cao, Xinhang Xu, Shenghai Yuan +3

We present the dynamic model and control of DoubleBee, a novel hybrid aerial-ground vehicle consisting of two propellers mounted on tilting servo motors and two motor-driven wheels…

cs.RO20233 cited

Learning-Based Defect Recognitions for Autonomous UAV Inspections

Kangcheng Liu

Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning fr…