Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM

TL;DR

This paper introduces a markerless 6D pose estimation method using deep learning to improve multi-robot SLAM by enhancing data association without fiducial markers, validated in a planetary environment.

Contribution

It presents a novel markerless 6D pose estimation approach integrated into multi-robot SLAM, overcoming limitations of fiducial markers under challenging lighting conditions.

Findings

Improved relative localization accuracy among robots.

Effective in environments with poor lighting or reflections.

Validated with real-world planetary analog data.

Abstract

The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of perceptual aliasing, or when perspectives differ significantly. For this reason, direct mutual observation among robots is a powerful way to connect partial SLAM graphs, but often relies on the presence of calibrated arrays of fiducial markers (e.g., AprilTag arrays), which severely limits the range of observations and frequently fails under sharp lighting conditions, e.g., reflections or overexposure. In this work, we propose a novel solution to this problem leveraging recent advances in Deep-Learning-based 6D pose estimation. We feature markerless pose estimation as part of a decentralized multi-robot SLAM system and demonstrate the benefit to the relative localization accuracy among the robotic team. The solution is validated experimentally on data recorded in a test field campaign on a planetary analogous environment.