Visual teach and repeat for long‐range rover autonomy

By Paul Timothy Furgale, T. Barfoot. 2010

354 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

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Citations in the corpus

Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, background): “Note that moving gates break the main assumption of traditional high-speed navigation approaches [1, 9], specifically that the trajectory can be pre-planned in a static world.”
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal (2018, background): “…mapless navigation, in which the agent navigates solely on the basis of its egocentric sensor modalities: mapping these inputs to actions with appearance-based matching and path recall [3], or by a process of implicit localisation and mapping in some form of latent representation [4, 5, 6].”
From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (2016, background): “During navigation, the goal is not only to replicate the provided expert demonstrations in one specific scenario as in teachand-repeat approaches [2], but rather to be able to learn collision avoidance strategies and transfer them to previously unseen real-world environments.”
Vision based navigation for micro helicopters (2012, background): “The robotics community showed that local navigation capabilities are sufficient for large scale robot applications (Furgale and Barfoot (2010)).”
It's not easy seeing green: Lighting-resistant stereo Visual Teach & Repeat using color-constant images (2015, positive): “In the next section, we describe our experiments using this colorspace transformation and a stereo camera that significantly violates these assumptions, as well as our strategy to address the issue through training data.”
Practice makes perfect? Managing and leveraging visual experiences for lifelong navigation (2012, methodology): “We now outline the requirements of the local navigation system before explaining how this is used to create the plastic map.”
Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking (2016, methodology): “vision-based localization (Furgale and Barfoot, 2010) for off-road”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “In [171], a two-step process is proposed.”
Visual Place Recognition: A Survey (2016, background): “While global descriptors are more pose dependent than local feature descriptors, local feature descriptors perform poorly when lighting conditions change [117] and are comprehensively outperformed by global descriptors at performing place recognition in changing conditions [118], [119].”
Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera (2016, methodology): “Furgale and Barfoot used SIFT features in their visual Teach and Repeat system and noted their lack of robustness to changes in the time of day [19].”