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|Title:||Structure aware SLAM using quadrics and planes|
|Citation:||Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science, 2019 / vol.11363, pp.410-426|
|Series/Report no.:||Lecture Notes in Computer Science ; 11363|
|Conference Name:||Asian Conference on Computer Vision (ACCV) (02 Dec 2018 - 06 Dec 2018 : Perth, Australia)|
|Mehdi Hosseinzadeh, Yasir Latif, Trung Pham, Niko Suenderhauf, and Ian Reid|
|Abstract:||Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art object detection methods provide rich information about entities present in the scene from a single image. This work marries the two and proposes a method for representing generic objects as quadrics which allows object detections to be seamlessly integrated in a SLAM framework. For scene coverage, additional dominant planar structures are modeled as infinite planes. Experiments show that the proposed points-planes-quadrics representation can easily incorporate Manhattan and object affordance constraints, greatly improving camera localization and leading to semantically meaningful maps.|
|Keywords:||Visual semantic SLAM; Object SLAM; Planes; Quadrics|
|Rights:||© Springer Nature Switzerland AG 2019|
|Appears in Collections:||Computer Science publications|
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