Learning meshes for dense visual SLAM
| Autor: | Andrew J. Davison, Tristan Laidlow, Stefan Leutenegger, Ronald Clark, Michael Bloesch |
|---|---|
| Přispěvatelé: | Dyson Technology Limited |
| Rok vydání: | 2019 |
| Předmět: |
Vertex (graph theory)
Artificial neural network Computer science business.industry Inference 02 engineering and technology Simultaneous localization and mapping 030218 nuclear medicine & medical imaging Vertex (geometry) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Polygon mesh Artificial intelligence business Representation (mathematics) Algorithm Factor graph |
| Zdroj: | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) ICCV |
| Popis: | Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem. From an information theoretic point of view, estimates should get better as more information is included, such as is done in dense SLAM, but this is strongly dependent on the validity of the underlying models. In the present paper, we use triangular meshes as both compact and dense geometry representation. To allow for simple and fast usage, we propose a view-based formulation for which we predict the in-plane vertex coordinates directly from images and then employ the remaining vertex depth components as free variables. Flexible and continuous integration of information is achieved through the use of a residual based inference technique. This so-called factor graph encodes all information as mapping from free variables to residuals, the squared sum of which is minimised during inference. We propose the use of different types of learnable residuals, which are trained end-to-end to increase their suitability as information bearing models and to enable accurate and reliable estimation. Detailed evaluation of all components is provided on both synthetic and real data which confirms the practicability of the presented approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|