How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels

Fischer, Tobias W.; Milford, Michael

doi:10.48550/arxiv.2206.13673

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…To implement sequence matching, we use the computationally efficient method of convolving the weighted distance matrix with an identity matrix with sequence length L, as described in [20] and [26]:…”

Section: B Anchored/weighted Sequencementioning

confidence: 99%

Predicting to Improve: Integrity Measures for Assessing Visual Localization Performance

Carson

Ford

Milford

2022

IEEE Robot. Autom. Lett.

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While substantial progress has been made in the absolute performance of localization and Visual Place Recognition (VPR) techniques, it is becoming increasingly clear from translating these systems into applications that other capabilities like integrity and predictability are just as important, especially for safety-or operationally-critical autonomous systems. In this research we present a new, training-free approach to predicting the likely quality of localization estimates, and a novel method for using these predictions to bias a sequence-matching process to produce additional performance gains beyond that of a naive sequence matching approach. Our combined system is lightweight, runs in real-time and is agnostic to the underlying VPR technique. On extensive experiments across four datasets and three VPR techniques, we demonstrate our system improves precision performance, especially at the high-precision/low-recall operating point. We also present ablation and analysis identifying the performance contributions of the prediction and weighted sequence matching components in isolation, and the relationship between the quality of the prediction system and the benefits of the weighted sequential matcher.

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Section: B Anchored/weighted Sequencementioning

confidence: 99%