Monocular Markerless 6D Pose Estimation
of ANYmal

TL;DR: We build an accurate markerless toolbox to localize and estimate pose of ANYmal.

Localization is an important task when it comes to tracking robots accurately in complicated and changeable environments. Previous methods generally rely on additional sources like depth cameras or QR codes placed in the surrounding environment. In this work, we propose to remove the dependency from these external sources by deploying state-of-the-art 6D pose estimation deep learning methods to achieve the same goal.

The way to achieve the final result comprises the following two building blocks:

Generate an accurate dataset including RGB images and ground truth 6D pose of the base of ANYmal;

Train a 6D pose estimation network on the generated dataset with supervised learning.

For detailed information, please refer to poster and report.

The ground truth poses of the base of ANYmal are generated by detecting AprilTags attached to a mounting fixed on top of the robot by leveraging the ROS package apriltag_ros. Then the position and quaternion is averaged between multiple faces detected in the same frame to reduce jitter.

We adapt one of the state-of-the-art method for 6D pose estimation called EfficientPose on the generated dataset.