On-Board Pose Estimation

This repository contains the code to perform pose estimation on the SPEED dataset. Refer to the repository found in https://github.com/masgura/SPEED to see how to preprocess the dataset and train the detection and regression models.

Dependencies

• OpenCV >= 4.0.0
• C++ >= 17.0
• Cmake >= 3.10
• OpenVINO >= 2024

To install OpenVINO you can refer to https://docs.openvino.ai/2024/get-started/install-openvino.html

To install OpenCV:

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.x.zip
unzip opencv.zip

mkdir -p build && cd build

cmake  ../opencv-4.x

cmake --build .

Build

To build the project:

mkdir build && cd build
cmake ..
make

Usage

This repository is based on the ESA Kelvins Pose Estimation Challenge and needs the test set from the SPEED dataset. In particular, the code in inference.cpp is used to process every image in the test set with two neural network:

1. YOLOv8 nano is used to detect the bounding box enclosing the Tango spacecraft.
2. The image zoomed on the Region of Interest (RoI) is then passed through a YOLOv8-pose nano model to regress the position of 11 keypoints.

Instead, slab_score.cpp is used to estimate translation and orientation of the spacecraft through the EPnP algorithm, and to compute the SLAB score on the test set.

To perform inference on the test set:

./inference <path/to/detection/model.xml> <path/to/regression/model.xml> <path/to/inference/file/save/file.json> <number of threads>

To compute the SLAB score:

./slab_score 

The code foun in run_all.sh can be used to measure CPU usage, CPU temperature and inference time at different number of threads and CPU frequency. The Linux cpufrequtils package is required:

sudo apt-get install cpufrequtils

Then the .sh can be launched with:

chmod +x run_all.sh
./run_all.sh