CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure
Convolutional Autoencoder for Loop Closure 2.0.
To get started, download the COCO dataset and the "stuff" annotations, then run dataset/gen_tfrecords.py
.
Make sure to unzip the tar in the dataset directory first.
Doing this will generate the sharded tfrecord files as well as loss_weights.txt
.
After that you can train with calc2.py
.
Check the --mode options in calc2.py to see what else you can do, like PR curves and finding the best model in a directory.
If you use this code for your research, please cite our paper:
@InProceedings{Merrill2019IROS,
Title = {{CALC2.0}: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure},
Author = {Nathaniel Merrill and Guoquan Huang},
Booktitle = {2019 International Conference on Intelligent Robots and Systems (IROS)},
Year = {2019},
Address = {Macau, China},
Month = nov,
}