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Bernhard Zeisl

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3 papers

1 author row

IROS Conference 2018 Conference Paper

LandmarkBoost: Efficient visualContext Classifiers for Robust Localization

Marcin Dymczyk
Igor Gilitschenski
Juan I. Nieto 0001
Simon Lynen
Bernhard Zeisl
Roland Siegwart

The growing popularity of autonomous systems creates a need for reliable and efficient metric pose retrieval algorithms. Currently used approaches tend to rely on nearest neighbor search of binary descriptors to perform the 2D-3D matching and guarantee realtime capabilities on mobile platforms. These methods struggle, however, with the growing size of the map, changes in viewpoint or appearance, and visual aliasing present in the environment. The rigidly defined descriptor patterns only capture a limited neighborhood of the keypoint and completely ignore the overall visual context. We propose LandmarkBoost - an approach that, in contrast to the conventional 2D-3D matching methods, casts the search problem as a landmark classification task. We use a boosted classifier to classify landmark observations and directly obtain correspondences as classifier scores. We also introduce a formulation of visual context that is flexible, efficient to compute, and can capture relationships in the entire image plane. The original binary descriptors are augmented with contextual information and informative features are selected by the boosting framework. Through detailed experiments, we evaluate the retrieval quality and performance of Landmark-Boost, demonstrating that it outperforms common state-of-the-art descriptor matching methods.

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ICRA Conference 2017 Conference Paper

Efficient descriptor learning for large scale localization

Antonio Loquercio
Marcin Dymczyk
Bernhard Zeisl
Simon Lynen
Igor Gilitschenski
Roland Siegwart

Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact map representations and improving retrieval is of considerable interest for enabling large-scale visual place recognition and loop-closure. In this paper, we propose a novel approach to compress descriptors while increasing their discriminability and match-ability, based on recent advances in neural networks. At the same time, we target resource-constrained robotics applications in our design choices. The main contributions of this work are twofold. First, we propose a linear projection from descriptor space to a lower-dimensional Euclidean space, based on a novel supervised learning strategy employing a triplet loss. Second, we show the importance of including contextual appearance information to the visual feature in order to improve matching under strong viewpoint, illumination and scene changes. Through detailed experiments on three challenging datasets, we demonstrate significant gains in performance over state-of-the-art methods.

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ICRA Conference 2016 Conference Paper

Structure-based auto-calibration of RGB-D sensors

Bernhard Zeisl
Marc Pollefeys

The readily available image and depth data from commodity RGB-D sensors has had tremendous impact in the robotics and computer vision community recently. To jointly leverage both modalities, the depth and image measurements need to be registered. Typical calibration approaches make use of artificial landmarks and special calibration targets. However, this is not feasible if on-line (re-)calibration is necessary or the sensor setup is inaccessible, e. g. , for already captured datasets. Instead of using specific calibration patterns, we propose to leverage a sparse environment model as geometric prior for the calibration. Structure-from-motion or SLAM can provide such a sparse 3D scene model, and hence our approach allows for self-calibration without the need for any manual interaction. We validate our hypothesis by introducing an optimization that jointly minimizes the alignment error between the sparse map and all recorded depth maps. Since the accuracy of depth measurements is known to degrade considerably with scene depth, we account for this distortion via a spatially varying correction term. The evaluation of our approach demonstrates that we are able to compute an accurate extrinsic and intrinsic calibration, which for example allows dense 3D modeling at improved precision.

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