Towards CNN Map Compression for camera relocalisation
This work addresses map compression for camera relocalization, which is incremental as it builds on state-of-the-art methods with a smaller CNN architecture.
The paper tackles camera relocalization by using Convolutional Neural Networks for map compression, achieving improved accuracy by increasing training trajectories while keeping the CNN size constant, as evaluated on public datasets.
This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate response to different data inputs -- namely, depth, grayscale, RGB, spatial position and combinations of these. We use a CNN map representation and introduce the notion of CNN map compression by using a smaller CNN architecture. We evaluate our proposal in a series of publicly available datasets. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.