IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

Christian Forster, Luca Carlone, Frank Dellaert, Davide Scaramuzza, "IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation", Robotics: Science and Systems (RSS), Rome, 2015.
PDF: http://rpg.ifi.uzh.ch/docs/RSS15_Fors...
Supplementary Material: http://rpg.ifi.uzh.ch/docs/RSS15_Fors...
Abstract:
Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango.

picsbuffet

picsbuffet is a visual image browsing system to visually explore and search millions of images from stock photo agencies and the like. Similar to map services like Google Maps users may navigate through multiple image layers by zooming and dragging. Zooming in (or out) shows more (or less) similar images from lower (or higher) levels. Dragging the view shows related images from the same level. Layers are organized as an image pyramid which is build using image sorting and clustering techniques. Easy image navigation is achieved because the placement of the images in the pyramid is based on an improved fused similarity calculation using visual and semantic image information. picbuffet also allows to perform searches. After starting an image search the user is automatically directed to a region with suiting results. Additional interesting regions on the map are shown on a heatmap.

picsbuffet 0.9 is the first publicly available version using over 1 million images from fotolia. Currently only the Chrome and Opera browser are supported. Future versions will support more images and other browsers as well. picsbuffet was developed by Radek Mackowiak, Nico Hezel and Prof. Dr. Kai Uwe Barthel at HTW Berlin (University of Applied Science).

picsbuffet could be used with other kind of images such as product photos and the like.

For further information about picsbuffet please contact Kai Barthel: barthel@htw-berlin.de

UAV survey and 3D reconstruction of the Schiefe Turm von Bad Frankenhausen with the AscTec Falcon 8

An Unsupervised Approach for Comparing Styles of Illustrations

Takahiko Furuya, Shigeru Kuriyama and Ryutarou Ohbuchi

In creating web pages, books, or presentation slides, consistent use of tasteful visual style(s) is quite important. In this paper, we consider the problem of style-based comparison and retrieval of illustrations. In their pioneering work, Garces et al. [2] proposed an algorithm for comparing illustrative style. The algorithm uses supervised learning that relied on stylistic labels present in a training dataset. In reality, obtaining such labels is quite difficult. In this paper, we propose an unsupervised approach to achieve accurate and efficient stylistic comparison among illustrations. The proposed algorithm combines heterogeneous local visual features extracted densely. These features are aggregated into a feature vector per illustration prior to be treated with distance metric learning based on unsupervised dimension reduction for saliency and compactness. Experimental evaluation of the proposed method by using multiple benchmark datasets indicates that the proposed method outperforms existing approaches.

http://www.kki.yamanashi.ac.jp/~ohbuchi/online_pubs/CBMI_2015_Style_Furuya_Kuriyama/CBMI2015_web.pdf

Beyond Vanilla Visual Retrieval

The presentation from professor Jiri Matas @ CBMI 2015

The talk will start with a brief overview of the state of the art in visual retrieval of specific objects. The core steps of the standard pipeline will be introduced and recent development improving both precision and recall as well as the memory footprint will be reviewed. Going off the beaten track, I will present a visual retrieval method applicable in conditions when the query and reference images differ significantly in one or more properties like illumination (day, night), the sensor (visible, infrared) , viewpoint, appearance (winter, summer), time of acquisition (historical, current) or the medium (clear, hazy, smoky). In the final part, I will argue that in image-based retrieval it might be often more interesting to look for most *dissimilar* images of the same scene rather than the most similar ones as conventionally done, as especially in large datasets these are just near duplicates.. As an example of such problem formulation, a method efficiently searching for images with the largest scale difference will be presented. A final demo will for instance show that the method finds surprisingly fine details on landmarks, even those that are hardly noticeable for human.

The presentation from professor Jiri Matas is available here.

NOPTILUS Final Experiment

The main objective of this experiment was to evaluate the performance of the extended and enhanced version of the NOPTILUS system on a large-scale, open-sea experiment. Operating in open-sea, especially in oceans, the navigation procedure faces several non-trivial problems, such as strong currents, limited communication, severe weather conditions etc.  Additionally, this is the first experiment in which we incorporate different sensor modalities. Half of the squad was equipped with single beams DVLs while the other half employed multi-beam sensors.
   
    In order to tackle the open-sea challenges, the new version of the NOPTILUS system incorporates an advanced motion control module that is capable to compensate strong currents, disturbances and turbulences.

   
    Moreover, the final version of the NOPTILUS system utilizes an improved version of the generic plug-n-play web-system, which allows the operation of larger squads. The developed tool is now capable to split the operation procedure in distinct, non-overlapped, timestamps. Based on the size of the squad, the web-system automatically schedules the transmission of the navigation instructions so as, on the one hand, to meet the available bandwidth requirements, while on the other side of the spectrum, to avoid possible congestion issues.
   
    To the best of our knowledge this is the first time that a heterogeneous squad of AUVs is capable of fully autonomous navigate in an unknown open sea area, in order to map the underwater surface of the benthic environment and simultaneously to track the movements of a moving target, in an cooperative fashion.

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