Abstract
Matthias Schorghuber, Daniel Steininger, Yohann Cabon, Martin Humenberger, Margrit Gelautz |
Workshop on Deep Learning for Visual SLAM at ICCV19, Seoul, South Korea, 27 October-2 November, 2019 |
Download |
@inproceedings{schorghuber2019slamantic, title={SLAMANTIC-Leveraging Semantics to Improve VSLAM in Dynamic Environments}, author={Schorghuber, Matthias and Steininger, Daniel and Cabon, Yohann and Humenberger, Martin and Gelautz, Margrit}, booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops}, pages={0--0}, year={2019} }
Abstract
In this paper, we tackle the challenge for VSLAM of handling non-static environments. We propose to include semantic information obtained by deep learning methods in the traditional geometric pipeline. Specifically, we compute a confidence measure for each map point as a function of its semantic class (car, person, building, etc.) and its detection consistency over time. The confidence is then applied to guide the usage of each point in the mapping and localization stage. Points with high confidence are used to verify points with low confidence in order to select the final set of points for pose computation and mapping. Furthermore, we can handle map points whose state may change between static and dynamic (a car can be parked or in motion). Evaluating our method on public datasets, we show that it can successfully solve challenging situations in dynamic environments which cause state-of-the-art baseline VSLAM algorithms to fail and that it maintains performance on static scenes. Code is available at http://github.com/mthz/slamantic.
1. Difference in female/male salary: 33/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: uncalculable
4. Number of employees in under-represented gender in 10 highest salaries: 0/10 points
NAVER France targets (with respect to the 2022 index) are as follows:
En 2022, NAVER France a obtenu les notes suivantes pour chacun des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 33 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : non calculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0 sur 10 points
Les objectifs de progression pour l’index 2022 de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
FunctionalThis website uses functional cookies which are required for the search function to work and to apply for jobs and internships.
AnalyticalOur website uses analytical cookies to make it possible to analyse our website and optimize its usability.
Social mediaOur website places social media cookies to show YouTube and Vimeo videos. Cookies placed by these sites may track your personal data.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.