activity
20182022
most citedSTA-VPR: Spatio-temporal Alignment for Visual Place Recognition

28 citations · 38 across the 7 of their papers we have counts for

collaborators

8 papers

cs.HC20221 cited

An Augmented Reality Application and User Study for Understanding and Learning Spatial Transformation Matrices

Zohreh Shaghaghian, Heather Burte, Dezhen Song +1

Understanding spatial transformations and their mathematical representations are essential in computer-aided design, robotics, etc. This research has developed and tested an Augmen…

cs.CV20212 cited

Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data

Shuangyu Xie, Chengsong Hu, Muthukumar Bagavathiannan +1

To enable robotic weed control, we develop algorithms to detect nutsedge weed from bermudagrass turf. Due to the similarity between the weed and the background turf, manual data la…

cs.HC20217 cited

Towards Learning Geometric Transformations through Play: An AR-powered approach

Zohreh Shaghaghian, Wei Yan, Dezhen Song

Despite the excessive developments of architectural parametric platforms, parametric design is often interpreted as an architectural style rather than a computational method. Also,…

cs.CV202128 cited

STA-VPR: Spatio-temporal Alignment for Visual Place Recognition

Feng Lu, Baifan Chen, Xiang-Dong Zhou +1

Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the midd…

cs.RO2020

Gaussian Processes Model-based Control of Underactuated Balance Robots

Kuo Chen, Jingang Yi, Dezhen Song

Ranging from cart-pole systems and autonomous bicycles to bipedal robots, control of these underactuated balance robots aims to achieve both external (actuated) subsystem trajector…

cs.RO2020

Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach

Hsin-Min Cheng, Dezhen Song

Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods a…