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20172026
most citedA Multi-Domain Feature Learning Method for Visual Place Recognition

5 citations · 10 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.RO2024★ 1 cited

General Place Recognition Survey: Towards Real-World Autonomy

Peng Yin, Jianhao Jiao, Shiqi Zhao +5

In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a corners…

cs.RO2022★ 2 cited

BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition

Peng Yin, Abulikemu Abuduweili, Shiqi Zhao +2

We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas. Unlike hum…

cs.RO2021

Graph-Guided Deformation for Point Cloud Completion

Jieqi Shi, Lingyun Xu, Liang Heng +1

For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is g…

cs.RO2019

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen +7

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving l…

cs.RO2019★ 5 cited

A Multi-Domain Feature Learning Method for Visual Place Recognition

Peng Yin, Lingyun Xu, Xueqian Li +6

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and…

cs.RO2018

Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection

Peng Yin, Yuqing He, Lingyun Xu +3

Loop Closure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are u…