Dense topological maps and partial pose estimation for visual control of an autonomous cleaning robot

Gerstmayr-Hillen, Lorenz; Röben, Frank; Krzykawski, Martin; Kreft, Sven; Venjakob, D.; Möller, Ralf

doi:10.1016/j.robot.2012.12.006

Cited by 18 publications

(13 citation statements)

References 105 publications

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“…Home vector and visual compass can either be used to approach a target location where the snapshot was taken (by following the home vectors in Section 5), or they can be used to obtain a position estimate by triangulating multiple snapshots with known positions (see Section 7). The latter application of min-warping is the basis of the visual lane-control method for domestic cleaning robots presented in [54,55]. Here min-warping performed well both in computer simulations with different environments (walls with random textures, walls with photos of real environments) and in robot experiments in unmodified lab rooms and apartments, at least under constant conditions of illumination.…”

Section: Min-warping a Holistic Local Visual Homing Methodsmentioning

confidence: 87%

“…Warping has been applied to navigation in topological maps [50,52,53]. The min-warping method [20] addressed in this paper has successfully been applied to cleaning robot control [54,55].…”

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

“…For these two reasons, holistic methods may be particularly suitable for consumer market robots (e.g., cleaning or lawn-mowing robots) and for flying robots which typically have limited onboard processing power due to constraints in cost, weight, and battery power. (3) Despite their simplicity, some holistic navigation models have been successfully applied to real-world images from outdoor environments [25,26,56] and for indoor navigation of cleaning robots [54,55]. At least for rotation estimation it has been observed that holistic methods perform better than feature-based approaches [57] which seem to be susceptible to calibration errors.…”

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

“…All possible applications of min-warping-like following the repeatedly updated home vector to approach a goal location, or triangulating image capture points for navigation in topological-metrical maps [54,55]-depend on the precision of the home vector estimates (note that home vectors depend on both estimated parameters α and ψ). We therefore directly evaluate the precision of home vector estimates using image databases.…”

Section: Image Databasesmentioning

confidence: 99%

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Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

2014

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Holistic visual navigation methods are an emerging alternative to the ubiquitous feature-based methods. Holistic methods match entire images pixel-wise instead of extracting and comparing local feature descriptors. In this paper we investigate which pixel-wise distance measures are most suitable for the holistic min-warping method with respect to illumination invariance. Two novel approaches are presented: tunable distance measures-weighted combinations of illumination-invariant and illumination-sensitive terms-and two novel forms of "sequential" correlation which are only invariant against intensity shifts but not against multiplicative changes. Navigation experiments on indoor image databases collected at the same locations but under different conditions of illumination demonstrate that tunable distance measures perform optimally by mixing their two portions instead of using the illumination-invariant term alone. Sequential correlation performs best among all tested methods, and as well but much faster in an approximated form. Mixing with an additional illumination-sensitive term is not necessary for sequential correlation. We show that min-warping with approximated sequential correlation can successfully be applied to visual navigation of cleaning robots.

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Section: Min-warping a Holistic Local Visual Homing Methodsmentioning

confidence: 87%

“…Warping has been applied to navigation in topological maps [50,52,53]. The min-warping method [20] addressed in this paper has successfully been applied to cleaning robot control [54,55].…”

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

Section: Image Databasesmentioning

confidence: 99%

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Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

2014

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“…Links between nodes indicate that the robot can travel between these nodes on a direct path. The density of nodes required to describe the environment can vary depending on the robot's task, ranging from sparse (e.g., one node per room) to dense (e.g., a node every few centimeters) [2,3].…”

Section: Introductionmentioning

confidence: 99%

Visual Place Recognition for Autonomous Mobile Robots

Horst

Möller

2017

Robotics

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Abstract:Place recognition is an essential component of autonomous mobile robot navigation. It is used for loop-closure detection to maintain consistent maps, or to localize the robot along a route, or in kidnapped-robot situations. Camera sensors provide rich visual information for this task. We compare different approaches for visual place recognition: holistic methods (visual compass and warping), signature-based methods (using Fourier coefficients or feature descriptors (able for binary-appearance loop-closure evaluation, ABLE)), and feature-based methods (fast appearance-based mapping, FabMap). As new contributions we investigate whether warping, a successful visual homing method, is suitable for place recognition. In addition, we extend the well-known visual compass to use multiple scale planes, a concept also employed by warping. To achieve tolerance against changing illumination conditions, we examine the NSAD distance measure (normalized sum of absolute differences) on edge-filtered images. To reduce the impact of illumination changes on the distance values, we suggest to compute ratios of image distances to normalize these values to a common range. We test all methods on multiple indoor databases, as well as a small outdoor database, using images with constant or changing illumination conditions. ROC analysis (receiver-operator characteristics) and the metric distance between best-matching image pairs are used as evaluation measures. Most methods perform well under constant illumination conditions, but fail under changing illumination. The visual compass using the NSAD measure on edge-filtered images with multiple scale planes, while being slower than signature methods, performs best in the latter case.

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A Robust Feature Matching Method for Robot Localization in a Dynamic Indoor Environment

Tsou

2014

Technologies and Applications of Artificial Intelligence

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Dense topological maps and partial pose estimation for visual control of an autonomous cleaning robot

Cited by 18 publications

References 105 publications

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

Visual Place Recognition for Autonomous Mobile Robots

A Robust Feature Matching Method for Robot Localization in a Dynamic Indoor Environment

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