Continual Learning for Multi-camera Relocalisation
| Autor: | Aldrich A. Cabrera-Ponce, Jose Martinez-Carranza, Manuel Martín-Ortíz |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Robotics Viewpoints Convolutional neural network Global Positioning System Computer vision Artificial intelligence Chromatic scale business Set (psychology) Geographic coordinate system computer computer.programming_language |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783030898168 MICAI (1) |
| Popis: | Visual relocalisation is a well-known problem in the robotics community, where chromatic images are used to recognise a place that is being re-visited or re-observed again. Due to the success of deep neural networks in several computer vision tasks, convolutional neural networks have been proposed to address the visual relocalisation problem as well. However, these solutions follow the conventional off-line training in order to generate a model that can be used to regress a camera’s pose w.r.t to an input image. In this work, we present a methodology based on continual learning to address the visual relocalisation problem aiming at performing on-line model training, seeking to generate a model that is updated continuously to learn new acquired images associated with GPS coordinates. Moreover, we apply this methodology to the multi-camera case, where 8 images are acquired from a multi-rig camera, seeking to improve the localisation accuracy, this is, by using a multi-camera, we obtain a set of images observing different viewpoints of the scene for a given GPS position. Therefore, by using a voting scheme, our on-line learned model is capable of performing visual relocalisation with an accuracy of 0.78, performing at 50 fps. |
| Databáze: | OpenAIRE |
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