Author name cluster

Shengquan Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Debiased Multiplex Tokenizer for Efficient Map-Free Visual Relocalization

Wenshuai Wang
Hong Liu
Shengquan Li
Peifeng Jiang
Runwei Ding

Image-based feature representation plays a critical role in visual localization, enabling robots to estimate their position and orientation in GPS-denied environments. However, this task is often undermined by significant variations in camera viewpoints and scene appearances. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm due to its compatibility with lightweight deployment and privacy isolation on mobile devices. In this paper, we propose the Debiased Multiplex Tokenizer (DeMT) as a novel method for versatile and efficient MFVR. Specifically, DeMT performs relative pose regression through an integrated framework built upon a pretrained vision Mamba encoder, comprising three key modules: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions; Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and causal pointer attribution; Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a gating mechanism and spectral normalization to support both pair-wise and multi-view modes. Extensive evaluations across nine public datasets demonstrate that DeMT substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments.

PDF Details DOI

IROS Conference 2024 Conference Paper

UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images

Zeyu Chen
Jingyi Tang
Gu Wang 0001
Shengquan Li
Xinghui Li
Xiangyang Ji
Xiu Li 0001

Due to the unique characteristics of underwater environments, accurate 3D reconstruction of underwater objects poses a challenging problem in tasks such as underwater exploration and mapping. Traditional methods that rely on multiple sensor data for 3D reconstruction are time-consuming and face challenges in data acquisition in underwater scenarios. We propose UW-SDF, a framework for reconstructing target objects from multi-view underwater images based on neural SDF. We introduce hybrid geometric priors to optimize the reconstruction process, markedly enhancing the quality and efficiency of neural SDF reconstruction. Additionally, to address the challenge of segmentation consistency in multi-view images, we propose a novel few-shot multi-view target segmentation strategy using the general-purpose segmentation model (SAM), enabling rapid automatic segmentation of unseen objects. Through extensive qualitative and quantitative experiments on diverse datasets, we demonstrate that our proposed method outperforms the traditional underwater 3D reconstruction method and other neural rendering approaches in the field of underwater 3D reconstruction.

Details

ICRA Conference 2021 Conference Paper

Design of a deployable underwater robot for the recovery of autonomous underwater vehicles based on origami technique

Jisen Li
Yuliang Yang
Yumei Zhang
Hua Zhu
Yongqi Li 0003
Qiujun Huang
Haibo Lu
Shan He

The recovery of autonomous underwater vehicles (AUVs) has been a challenging mission due to the limited localization accuracy and movement capability of the AUVs. To overcome these limitations, we propose a novel design of a deployable underwater robot (DUR) for the recovery mission. Utilizing the origami structure, the DUR can transform between open and closed states to maximize the performance at different recovery stages. At the approaching stage, the DUR will remain closed state to reduce the drag force. While at the capturing state, the DUR will deploy to form a much larger opening to improve the success rate of docking. Meanwhile, the thrusters’ configuration also changes with the transformation of the robot body. The DUR can achieve a high driven force in the forward direction with the closed state which leads to a fast-approaching speed. While with the open state, the DUR can achieve more balanced force and torque maneuverability to prepare for agile position adjustment for the docking. CFD simulation has been used to analyze the drag forces and identify the hydrodynamic coefficients. A prototype of the robot has been fabricated and tested in an indoor water pool. Both simulation and experiment results validate the feasibility of the proposed design.

Details