112 citations · 211 across the 21 of their papers we have counts for
11 papers
Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
Chenghao Shi, Xieyuanli Chen, Junhao Xiao +2
Loop closing and relocalization are crucial techniques to establish reliable and robust long-term SLAM by addressing pose estimation drift and degeneration. This article begins by…
Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation
Benedikt Mersch, Tiziano Guadagnino, Xieyuanli Chen +3
Mobile robots that navigate in unknown environments need to be constantly aware of the dynamic objects in their surroundings for mapping, localization, and planning. It is key to r…
RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
Chenghao Shi, Xieyuanli Chen, Huimin Lu +3
Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show…
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
Junyuan Deng, Xieyuanli Chen, Songpengcheng Xia +4
Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-base…
Hybrid Map-Based Path Planning for Robot Navigation in Unstructured Environments
Jiayang Liu, Xieyuanli Chen, Junhao Xiao +3
Fast and accurate path planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured outdoor environments. However, most existing meth…
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
Wenbang Deng, Kaihong Huang, Qinghua Yu +3
Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these…