Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps

TL;DR

This paper introduces a novel depth-based 3D robot pose estimation method using Semi-Perspective Decoupled Heatmaps, trained on synthetic data, achieving state-of-the-art results without domain adaptation.

Contribution

It proposes a new pose representation and a depth map input approach that enables accurate 3D robot pose estimation from external depth sensors without internal hardware access.

Findings

Outperforms current state-of-the-art methods

Effective training on synthetic data without domain adaptation

Works with any robot using external depth devices

Abstract

Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes. In this paper, we propose a non-invasive and light-invariant framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera. The method can be applied to any robot without requiring hardware access to the internal states. We introduce a novel representation of the predicted pose, namely Semi-Perspective Decoupled Heatmaps (SPDH), to accurately compute 3D joint locations in world coordinates adapting efficient deep networks designed for the 2D Human Pose Estimation. The proposed approach, which takes as input a depth representation based on XYZ coordinates, can be trained on synthetic depth data and applied to real-world settings without the need for domain adaptation techniques. To this end, we present the SimBa dataset, based on both synthetic and real depth images, and use it for the experimental evaluation. Results show that the proposed approach, made of a specific depth map representation and the SPDH, overcomes the current state of the art.