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 the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.
- Pub Date:
- September 2017
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Robotics
- Submitted to the 1st International Workshop on Deep Learning for Visual SLAM, at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)