activity
20182021
most citedEvent-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition

2 citations · 3 across the 2 of their papers we have counts for

collaborators

6 papers

cs.RO20211 cited

Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment

Rui Tian, Yunzhou Zhang, Yonghui Feng +4

Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this paper, we propose a stereo visual SLAM with a robust quadric landmark representation method…

q-bio.QM2021

Rough Set Microbiome Characterisation

Benjamin Wingfield, Sonya Coleman, T. M. McGinnity +1

Microbiota profiles measure the structure of microbial communities in a defined environment (known as microbiomes). In the past decade, microbiome research has focused on health ap…

cs.CV2021

Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry

Rui Tian, Yunzhou Zhang, Delong Zhu +3

Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like se…

cs.CV20202 cited

Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition

Delei Kong, Zheng Fang, Haojia Li +3

Traditional visual place recognition (VPR) methods generally use frame-based cameras, which is easy to fail due to dramatic illumination changes or fast motions. In this paper, we…

cs.RO2020

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Yanmin Wu, Yunzhou Zhang, Delong Zhu +3

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, w…

cs.CV2018

PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing

Diederik Paul Moeys, Daniel Neil, Federico Corradi +7

Machine vision systems using convolutional neural networks (CNNs) for robotic applications are increasingly being developed. Conventional vision CNNs are driven by camera frames at…