Ben Harwood — accepted AI-conference papers

2021

Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood, Muming Zhao, Sridha Sridharan, Clinton Fookes

ICRA 2021poster

Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a m…

Cited by 91 Source ↗code ↗Scholar ↗

2020

Delta Descriptors: Change-Based Place Representation for Robust Visual Localization

Sourav Garg, Ben Harwood, Gaurangi Anand, Michael Milford

RA-L 2020

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-l

Cited by 48 Source ↗code ↗Scholar ↗

2017

Smart Mining for Deep Metric Learning

Ben Harwood, Vijay Kumar B G, Gustavo Carneiro, Ian Reid, Tom Drummond

ICCV 2017poster

To solve deep metric learning problems and produce feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the train…

Cited by 409 PDF ↗Scholar ↗

2016

FANNG: Fast Approximate Nearest Neighbour Graphs

Ben Harwood, Tom Drummond

CVPR 2016spotlight

We present a new method for approximate nearest neighbour search on large datasets of high dimensional feature vectors, such as SIFT or GIST descriptors. Our approach constructs a directed graph that can be efficiently explored for nearest neighbour queries. Each vertex in this graph represents a f…

Cited by 172 PDF ↗Scholar ↗