activity
20172025
most citedSUM: Sequential Scene Understanding and Manipulation

2 citations · 2 across the 1 of their papers we have counts for

collaborators

6 papers

cs.RO2025

An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization

Changhong Lin, Jiarong Lin, Zhiqiang Sui +4

Multi-sensor fusion is essential for autonomous vehicle localization, as it is capable of integrating data from various sources for enhanced accuracy and reliability. The accuracy…

cs.RO2020

GeoFusion: Geometric Consistency informed Scene Estimation in Dense Clutter

Zhiqiang Sui, Haonan Chang, Ning Xu +1

We propose GeoFusion, a SLAM-based scene estimation method for building an object-level semantic map in dense clutter. In dense clutter, objects are often in close contact and seve…

cs.RO2019

GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments

Xiaotong Chen, Rui Chen, Zhiqiang Sui +4

Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and r…

cs.RO2018

Never Mind the Bounding Boxes, Here's the SAND Filters

Zhiqiang Sui, Zhefan Ye, Odest Chadwicke Jenkins

Perception is the main bottleneck to perform autonomous mobile manipulation tasks, especially in cluttered and unstructured environment. In this paper, we propose a novel two-stage…

cs.RO2018

Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency

Zheming Zhou, Zhiqiang Sui, Odest Chadwicke Jenkins

In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials. Translucency poses several open challen…

cs.RO20172 cited

SUM: Sequential Scene Understanding and Manipulation

Zhiqiang Sui, Zheming Zhou, Zhen Zeng +1

In order to perform autonomous sequential manipulation tasks, perception in cluttered scenes remains a critical challenge for robots. In this paper, we propose a probabilistic appr…