Simple loop closure for Visual SLAM
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Latest commit ad4d098 Jul 8, 2018

README.md

Simple loop closure for Visual SLAM

Possibily the simplest example of loop closure for Visual SLAM. More information [on my blog](http://nicolovaligi.com/bag-of-words-loop-closure- visual-slam.html).

As I'm experimenting with alternative approaches for SLAM loop closure, I wanted a baseline that was reasonably close to state-of-the art approaches. The approach here is pretty similar to ORB-SLAM's, and uses SURF descriptors and bag of words to translate them to a global image description vector.

The dataset

For testing, I've used the New College dataset published alongside FAB-MAP. It's available for download here. It's ideal for loop-closure testing, since it includes manual place associations that can be used for evaluation.

Building with Docker

You can build and run the code using docker-compose and Docker. The Docker configuration uses a Ubuntu 16.04 base image, and builds OpenCV 3 from source.

# Will take ~10 minutes to download and build OpenCV 3
docker-compose build runner
docker-compose run runner bash

# You're now in a shell inside the Docker container:
mkdir build
cd build
cmake ..
make -j$(nproc)

./new_college

Compatibility

Only tested on Ubuntu 16.04 LTS with OpenCV3, gcc 5.4.0

Plotting the confusion matrix

The ground_truth_comparison.py plots and compares the loop closures from the ground truth to the actual results from the code.