Search Results for author: Joshua Knights

Found 7 papers, 4 papers with code

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

1 code implementation31 Aug 2023 Milad Ramezani, Liang Wang, Joshua Knights, Zhibin Li, Pauline Pounds, Peyman Moghadam

This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition methods.

Domain Adaptation Retrieval

GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors

no code implementations9 Aug 2023 Joshua Knights, Stephen Hausler, Sridha Sridharan, Clinton Fookes, Peyman Moghadam

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 datasets, often requiring re-training the networks to achieve peak performance.

Test-time Adaptation

Uncertainty-Aware Lidar Place Recognition in Novel Environments

1 code implementation4 Oct 2022 Keita Mason, Joshua Knights, Milad Ramezani, Peyman Moghadam, Dimity Miller

State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments.

InCloud: Incremental Learning for Point Cloud Place Recognition

2 code implementations2 Mar 2022 Joshua Knights, Peyman Moghadam, Milad Ramezani, Sridha Sridharan, Clinton Fookes

In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space.

Incremental Learning

Point Cloud Segmentation Using Sparse Temporal Local Attention

no code implementations1 Dec 2021 Joshua Knights, Peyman Moghadam, Clinton Fookes, Sridha Sridharan

Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment.

Autonomous Vehicles Point Cloud Segmentation

Temporally Coherent Embeddings for Self-Supervised Video Representation Learning

1 code implementation21 Mar 2020 Joshua Knights, Ben Harwood, Daniel Ward, Anthony Vanderkop, Olivia Mackenzie-Ross, Peyman Moghadam

The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.

Metric Learning Representation Learning +3

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