Proceedings ArticleDOI
Visual triage: A bag-of-words experience selector for long-term visual route following
Kirk MacTavish,Michael Paton,Timothy D. Barfoot +2 more
- pp 2065-2072
TLDR
This paper introduces an algorithm that prioritizes experiences most relevant to live operation, limiting the number of experiences required for localization, and demonstrates safe, vision-in-the-loop route following over a 31 hour period, despite appearance as different as night and day.Abstract:
Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous navigation system that enables the rapid construction of route networks, safely built through operator-controlled driving. Added routes can be followed autonomously using visual localization. To enable long-term operation that is robust to appearance change, its Multi-Experience Localization (MEL) leverages many previously driven experiences when localizing to the manually taught network. While this multi-experience method is effective across appearance change, the computation becomes intractable as the number of experiences grows into the tens and hundreds. This paper introduces an algorithm that prioritizes experiences most relevant to live operation, limiting the number of experiences required for localization. The proposed algorithm uses a visual Bag-of-Words description of the live view to select relevant experiences based on what the vehicle is seeing right now, without having to factor in all possible environmental influences on scene appearance. This system runs in the loop, in real time, does not require bootstrapping, can be applied to any pointfeature MEL paradigm, and eliminates the need for visual training using an online, local visual vocabulary. By picking a subset of visually similar experiences to the live view, we demonstrate safe, vision-in-the-loop route following over a 31 hour period, despite appearance as different as night and day.read more
Citations
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Proceedings ArticleDOI
Navigation without localisation: reliable teach and repeat based on the convergence theorem
TL;DR: A position error model of a robot, which traverses a taught path by only correcting its heading is established, and a mathematical proof is outlined which shows that this position error does not diverge over time.
Journal ArticleDOI
kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation.
TL;DR: It is proved that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade.
Book ChapterDOI
I Can See for Miles and Miles: An Extended Field Test of Visual Teach and Repeat 2.0
TL;DR: Visual Teach and Repeat 2.0 is described, a vision-based path-following system capable of safe, long-term navigation over large-scale networks of connected paths in unstructured, outdoor environments and validated experimentally through an eleven-day field test in an untended gravel pit in Sudbury, Canada.
Journal ArticleDOI
Selective memory: Recalling relevant experience for long‐term visual localization
TL;DR: It is shown that the combination of the novel methods presented in this paper enable full use of incredibly rich multiexperience maps, opening the door to robust long‐term visual localization.
Posted Content
Navigation without localisation: reliable teach and repeat based on the convergence theorem
TL;DR: In this paper, the authors present a simple monocular teach-and-repeat navigation method based on the insights from the model, which is computationally efficient, it does not require camera calibration and can learn and autonomously traverse arbitrarily-shaped paths.
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