4 citations · 7 across the 10 of their papers we have counts for
11 papers
GWAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation
Ke Gui, Chenghao Shi, Ziyue Wang +3
World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies beyond the current field of view,…
GeoLoco: Leveraging 3D Geometric Priors from Visual Foundation Model for Robust RGB-Only Humanoid Locomotion
Yufei Liu, Xieyuanli Chen, Hainan Pan +5
The prevailing paradigm of perceptive humanoid locomotion relies heavily on active depth sensors. However, this depth-centric approach fundamentally discards the rich semantic and…
Efficient Image-Goal Navigation with Representative Latent World Model
Zhiwei Zhang, Hui Zhang, Kaihong Huang +2
World models enable robots to conduct counterfactual reasoning in physical environments by predicting future world states. While conventional approaches often prioritize pixel-leve…
BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model
Ziyue Wang, Chenghao Shi, Neng Wang +3
Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently,…
Image-Goal Navigation Using Refined Feature Guidance and Scene Graph Enhancement
Zhicheng Feng, Xieyuanli Chen, Chenghao Shi +4
In this paper, we introduce a novel image-goal navigation approach, named RFSG. Our focus lies in leveraging the fine-grained connections between goals, observations, and the envir…
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…
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
Neng Wang, Ruibin Guo, Chenghao Shi +5
4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous v…
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
Kai Luan, Chenghao Shi, Neng Wang +3
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdo…
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…
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…
InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
Neng Wang, Chenghao Shi, Ruibin Guo +3
Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a no…