451 research outputs found
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs
3D scene graph prediction is a task that aims to concurrently predict object
classes and their relationships within a 3D environment. As these environments
are primarily designed by and for humans, incorporating commonsense knowledge
regarding objects and their relationships can significantly constrain and
enhance the prediction of the scene graph. In this paper, we investigate the
application of commonsense knowledge graphs for 3D scene graph prediction on
point clouds of indoor scenes. Through experiments conducted on a real-world
indoor dataset, we demonstrate that integrating external commonsense knowledge
via the message-passing method leads to a 15.0 % improvement in scene graph
prediction accuracy with external knowledge and with internal
knowledge when compared to state-of-the-art algorithms. We also tested in the
real world with 10 frames per second for scene graph generation to show the
usage of the model in a more realistic robotics setting.Comment: accepted at CASE 202
Measurement errors in visual servoing
Abstract β In recent years, a number of hybrid visual servoing control algorithms have been proposed and evaluated. For some time now, it has been clear that classical control approaches β image and position based β- have some inherent problems. Hybrid approaches try to combine them in order to overcome these problems. However, most of the proposed approaches concentrate mainly on the design of the control law, neglecting the issue of errors resulting from the sensory system. This work deals with the effect of measurement errors in visual servoing. The particular contribution of this paper is the analysis of the propagation of image error through pose estimation and visual servoing control law. We have chosen to investigate the properties of the vision system and their effect to the performance of the control system. Two approaches are evaluated: i) position, and ii) 2 1/2 D visual servoing. We believe that our evaluation offers a valid tool to build and analyze hybrid control systems based on, for example, switching [1] or partitioning [2]. I
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