Image Sequence Localizer

This project proposes a framework for outdoor visual place recognition.

It can recognize visually similar places in GPS-denied environments. Thus, can serve as a candidate search technique in the context of full 6 DoF robot pose estimation.

Visual place recognition, also known as "weak localization", is performed here by matching sequences of images. The assumption is then that the input is a sequence of images. The program outputs the ids of image pairs that represent the same place. The matching pairs are also visualized as concatenated images.

Build

Prerequisites:

sudo apt-get install -y build-essential libopencv-dev libyaml-cpp-dev libprotobuf-dev libprotoc-dev protobuf-compiler

Tested on Ubuntu 20.04.

To build the code:

mkdir build
cd build
cmake ..
make -j4

To be able to use the python part, for example for visualization, I recommend setting up a virtual environment of your choice and installing the provided requirements through:

pip install -r requirements.txt

Requires Python 3.8+.

Usage

The code is under continuous development but at any point in time you should be able to run the matching procedure through:

cd src/python
python run_matching.py \
    --query_images <path_to_images> \
    --reference_images <path_to_images> \
    --dataset_name <dataset_name> \
    --output_dir <path_to_folder>
    --write_image_matches

The framework assumes that there is a query image sequence, for every image of which the user wants to find the corresponding image in the reference image sequence.

The run_matching.py script stores all the results in the user-provided output_dir. The user also needs to specify the name of the dataset, for example, "my_awesome_dataset".

For more details about the parameters, please use python run_matching.py --help.

For more details about the underlying method and the interpretation of the results, please have a look at paper. Here is a sketch of what roughly is happening for those who don't like to read much

Parent project

This repository is a continuation of my previous works vpr_relocalization and online_place_recognition.

The plan is to gradually modernize and improve the code by preserving the essential capabilities of the system.

Essential capabilities:

1. Given two sequences of images compute the matching image pairs.
2. Scripts to visualize the results.