Skip to content
CNNs for Loop-Closure Detection on the Oxford New College and City Centre Datasets
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
LICENSE Initial commit May 6, 2018 Initial commit May 6, 2018

CNNs for Loop-Closure Detection

The following is based on the methodology proposed in "Loop closure detection for visual SLAM systems using convolutional neural network" (see citation below). Various CNN architectures are available for method evaluation on the Oxford New College and City Centre datasets. The code can easily be extended for additional datasets and CNNs.


X. Zhang, Y. Su and X. Zhu, "Loop closure detection for visual SLAM systems using convolutional neural network," 2017 23rd International Conference on Automation and Computing (ICAC), Huddersfield, 2017, pp. 1-6. doi: 10.23919/IConAC.2017.8082072


NOTE: The Overfeat model may not work as it can give the same output for all inputs (see this issue)

The main script is and offers the following options.

python --help
usage: [-h] [--dataset DATASET] [--overfeat OVERFEAT]
                  [--weights_dir WEIGHTS_DIR] [--weights_base WEIGHTS_BASE]
                  [--layer LAYER] [--plot_gt] [--cluster] [--sweep_median]

CNNs for loop-closure detection.

positional arguments:
  model                 Model name: [overfeat, inception_v{1,2,3,4}, nasnet,

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     Either "city" or "college".
  --overfeat OVERFEAT   0 for small network, 1 for large
  --weights_dir WEIGHTS_DIR
                        Weights directory.
  --weights_base WEIGHTS_BASE
                        Basename of weights file.
  --layer LAYER         Layer number to extract features from.
  --plot_gt             Plots heat-map of ground truth and exits
  --cluster             Additionally performs clustering on sim matrix.
  --sweep_median        Sweep median filter size values.
  --debug               Use small number of images to debug code

Note that you need to run the script with python2 to use the OverFeat model, and python3 to use the TensorFlow models.


The following Python packages are needed (installed with pip, conda, etc.):

  • tensorflow
  • numpy
  • scipy
  • skimage
  • matplotlib
  • sklearn

Additionally, in order to use the Overfeat model, you'll need to install the Python API provided here. The GPU version should ideally be installed, but the authors only provide the source for the CPU version. OverFeat also has a TensorFlow implementation, but does not offer pre-trained checkpoint files. The repository provides package installation instructions.

In order to use the TensorFlow models (everything but OverFeat), you will need to clone the TensorFlow Slim model repository. The easiest way to do so is to clone both this repository and the TensorFlow models repository to the same directory:

git clone
git clone <THIS REPO>

Directory structure should look like this:

+ models/
|--- ...
|--- slim
|--- ...
| ...
You can’t perform that action at this time.