MapNet: An Allocentric Spatial Memory for Mapping Environments

This is a PyTorch re-implementation of MapNet, presented in:

João F. Henriques and Andrea Vedaldi, "MapNet: An Allocentric Spatial Memory for Mapping Environments", CVPR 2018 (PDF)

It reproduces all of the training from scratch for the mazes experiments, but not the Doom or AVD experiments; I hope to change that in the future.

Requirements

Although it may work with older versions, this has mainly been tested with:

• PyTorch 1.3
• Python 3.7
• OverBoard 0.1.4 (for plotting and visualization)

Usage

The mazes are stored in a large text file (45 MB). For this reason, it is zipped in data/maze/mazes-10-10-100000.zip (6 MB), please extract its contents to the same directory.

Training can then be performed by running train_mapnet.py. Run train_mapnet.py --help for command-line options and their explanation.

Visualization

Plots and tensor visualizations (mostly heatmaps of the joint position-orientation probability, as well as the maps) from OverBoard:

Author

João F. Henriques