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Omnidirectional vision based topological navigation

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TLDR
A novel system for autonomous mobile robot navigation with only an omnidirectional camera as sensor is presented, able to build automatically and robustly accurate topologically organised environment maps of a complex, natural environment.
Abstract
In this work we present a novel system for autonomous mobile robot navigation. With only an omnidirectional camera as sensor, this system is able to build automatically and robustly accurate topologically organised environment maps of a complex, natural environment. It can localise itself using such a map at each moment, including both at startup (kidnapped robot) or using knowledge of former localisations. The topological nature of the map is similar to the intuitive maps humans use, is memory-efficient and enables fast and simple path planning towards a specified goal. We developed a real-time visual servoing technique to steer the system along the computed path. A key technology making this all possible is the novel fast wide baseline feature matching, which yields an efficient description of the scene, with a focus on man-made environments.

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Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

TL;DR: An integrated approach to ‘loop-closure’, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path, is described, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment.
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Large-Scale 6-DOF SLAM With Stereo-in-Hand

TL;DR: A system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor, which accommodates both monocular and stereo.
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Visual teach and repeat for long-range rover autonomy

TL;DR: Because it enables long-range autonomous behavior in a single command cycle, visual teach and repeat is well suited to planetary applications, such as Mars sample return, in which no GPS is available.
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Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment

TL;DR: A new relative bundle adjustment is derived which, instead of optimizing in a single Euclidean space, works in a metric space defined by a manifold, and it is shown experimentally that it is possible to solve for the full maximum-likelihood solution incrementally in constant time, even at loop closure.
Journal ArticleDOI

Vision-based topological mapping and localization methods

TL;DR: This paper reviews the main solutions presented in the last fifteen years of topological mapping and localization methods, and classify them in accordance to the kind of image descriptor employed, including global, local, BoW and combinations.
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