501 citations · 1.9k across the 56 of their papers we have counts for
25 papers · 1 filter
NeuroVE: Brain-inspired Linear-Angular Velocity Estimation with Spiking Neural Networks
Xiao Li, Xieyuanli Chen, Ruibin Guo +4
Vision-based ego-velocity estimation is a fundamental problem in robot state estimation. However, the constraints of frame-based cameras, including motion blur and insufficient fra…
GOReloc: Graph-based Object-Level Relocalization for Visual SLAM
Yutong Wang, Chaoyang Jiang, Xieyuanli Chen
This article introduces a novel method for object-level relocalization of robotic systems. It determines the pose of a camera sensor by robustly associating the object detections i…
BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles
Lun Luo, Si-Yuan Cao, Xiaorui Li +4
This article introduces BEVPlace++, a novel, fast, and robust LiDAR global localization method for unmanned ground vehicles. It uses lightweight convolutional neural networks (CNNs…
SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
Neng Wang, Xieyuanli Chen, Chenghao Shi +3
Loop closing is a crucial component in SLAM that helps eliminate accumulated errors through two main steps: loop detection and loop pose correction. The first step determines wheth…
RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation
Changsong Pang, Xieyuanli Chen, Yimin Liu +2
Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities for mobile systems to achieve full autonomy. Several approaches have attempted to achieve…
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…
Deep Reinforcement Learning for Flipper Control of Tracked Robots
Hainan Pan, Bailiang Chen, Kaihong Huang +3
The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely…
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…
NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering
Liren Jin, Xieyuanli Chen, Julius Rückin +1
Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. In this paper, we address the problem of positioning an RGB camera to co…
A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems
Huan Yin, Xuecheng Xu, Sha Lu +5
Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR s…
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…
IR-MCL: Implicit Representation-Based Online Global Localization
Haofei Kuang, Xieyuanli Chen, Tiziano Guadagnino +3
Determining the state of a mobile robot is an essential building block of robot navigation systems. In this paper, we address the problem of estimating the robots pose in an indoor…
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
Hao Dong, Xieyuanli Chen, Simo Särkkä +1
Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for…
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
Benedikt Mersch, Xieyuanli Chen, Ignacio Vizzo +3
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and…
OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
Junyi Ma, Jun Zhang, Jintao Xu +3
Place recognition is an important capability for autonomously navigating vehicles operating in complex environments and under changing conditions. It is a key component for tasks s…
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…
Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments
Hao Dong, Xieyuanli Chen, Cyrill Stachniss
Reliable and accurate localization is crucial for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, lamps, etc., are ideal landmarks for localization in u…
Range Image-based LiDAR Localization for Autonomous Vehicles
Xieyuanli Chen, Ignacio Vizzo, Thomas Läbe +2
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of…
Simple But Effective Redundant Odometry for Autonomous Vehicles
Andrzej Reinke, Xieyuanli Chen, Cyrill Stachniss
Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or…
Learning an Overlap-based Observation Model for 3D LiDAR Localization
Xieyuanli Chen, Thomas Läbe, Lorenzo Nardi +2
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data…
OverlapNet: Loop Closing for LiDAR-based SLAM
Xieyuanli Chen, Thomas Läbe, Andres Milioto +5
Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based o…
SuMa++: Efficient LiDAR-based Semantic SLAM
Xieyuanli Chen, Andres Milioto, Emanuele Palazzolo +3
Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an imp…