90 citations · 116 across the 12 of their papers we have counts for
13 papers
VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition
Ahmad Khaliq, Ming Xu, Stephen Hausler +2
Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often formulated as an image retrieval task aimed at jointly…
GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors
Joshua Knights, Stephen Hausler, Sridha Sridharan +2
LiDAR place recognition approaches based on deep learning suffer from significant performance degradation when there is a shift between the distribution of training and test datase…
DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions
Stephen Hausler, Sourav Garg, Punarjay Chakravarty +3
Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior m…
Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization
Stephen Hausler, Sourav Garg, Punarjay Chakravarty +3
Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects…
Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique
Connor Malone, Stephen Hausler, Tobias Fischer +1
One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods…
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
Stephen Hausler, Ming Xu, Sourav Garg +4
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map. Current techniques use hierarch…
Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition
Stephen Hausler, Tobias Fischer, Michael Milford
A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, s…
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
Stephen Hausler, Sourav Garg, Ming Xu +2
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing…
Hierarchical Multi-Process Fusion for Visual Place Recognition
Stephen Hausler, Michael Milford
Combining multiple complementary techniques together has long been regarded as a way to improve performance. In visual localization, multi-sensor fusion, multi-process fusion of a…
Filter Early, Match Late: Improving Network-Based Visual Place Recognition
Stephen Hausler, Adam Jacobson, Michael Milford
CNNs have excelled at performing place recognition over time, particularly when the neural network is optimized for localization in the current environmental conditions. In this pa…
Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods
Stephen Hausler, Adam Jacobson, Michael Milford
Typical attempts to improve the capability of visual place recognition techniques include the use of multi-sensor fusion and integration of information over time from image sequenc…
Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation
Sourav Garg, Madhu Babu, Thanuja Dharmasiri +5
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Parti…
Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration
Stephen Hausler, Adam Jacobson, Michael Milford
Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition…