Simple loop closure for Visual SLAM

Possibily the simplest example of loop closure for Visual SLAM. More information on my blog.

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. The scripts/download_data.sh will download the data files (bag of words vocabulary and images) needed to run the code.

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.

# Download the data files
./scripts/download_data.sh

# Will take ~10 minutes to download and build OpenCV 3
docker-compose build runner
# Enter the docker shell
docker-compose run runner bash
# You're now in a shell inside the Docker container, build and run the code:
./scripts/build.sh
./build/new_college ./data/brief_k10L6.voc.gz ./data

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.