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Knowing the exact 3D location of workers and robots in a collaborativeenvironment enables several real applications, such as the detection of unsafesituations or the study of mutual interactions for statistical and socialpurposes. In this paper, we propose a non-invasive and light-invariantframework based on depth devices and deep neural networks to estimate the 3Dpose of robots from an external camera. The method can be applied to any robotwithout requiring hardware access to the internal states. We introduce a novelrepresentation of the predicted pose, namely Semi-Perspective DecoupledHeatmaps (SPDH), to accurately compute 3D joint locations in world coordinatesadapting efficient deep networks designed for the 2D Human Pose Estimation. Theproposed approach, which takes as input a depth representation based on XYZcoordinates, can be trained on synthetic depth data and applied to real-worldsettings without the need for domain adaptation techniques. To this end, wepresent the SimBa dataset, based on both synthetic and real depth images, anduse it for the experimental evaluation. Results show that the proposedapproach, made of a specific depth map representation and the SPDH, overcomesthe current state of the art.

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