most citedSuMa++: Efficient LiDAR-based Semantic SLAM

501 citations · 894 across the 9 of their papers we have counts for

collaborators

10 papers

cs.RO2022

Long-Term Localization using Semantic Cues in Floor Plan Maps

Nicky Zimmerman, Tiziano Guadagnino, Xieyuanli Chen +2

Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor en…

cs.CV20222 cited

Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors

Hao Dong, Xieyuanli Chen, Mihai Dusmanu +3

A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual…

cs.CV20221 cited

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Junyi Ma, Xieyuanli Chen, Jingyi Xu +1

Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based…

cs.CV2022

Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod

Si Yang, Lihua Zheng, Xieyuanli Chen +3

The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning…

cs.RO2022104 cited

Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation

Xieyuanli Chen, Benedikt Mersch, Lucas Nunes +4

Understanding the scene is key for autonomously navigating vehicles and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient fo…

cs.CV202113 cited

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

Benedikt Mersch, Xieyuanli Chen, Jens Behley +1

Exploiting past 3D LiDAR scans to predict future point clouds is a promising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and…