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For safe and sophisticated physical human-robot interactions (pHRI), a robotneeds to estimate the accurate body pose or mesh of the target person. However,in these pHRI scenarios, the robot cannot fully observe the target person'sbody with equipped cameras because the target person is usually close to therobot. This leads to severe truncation and occlusions, and results in pooraccuracy of human pose estimation. For better accuracy of human pose estimationor mesh recovery on this limited information from cameras, we propose an activemeasurement and sensor fusion framework of the equipped cameras and othersensors such as touch sensors and 2D LiDAR. These touch and LiDAR sensing areobtained attendantly through pHRI without additional costs. These sensormeasurements are sparse but reliable and informative cues for human meshrecovery. In our active measurement process, camera viewpoints and sensorplacements are optimized based on the uncertainty of the estimated pose, whichis closely related to the truncated or occluded areas. In our sensor fusionprocess, we fuse the sensor measurements to the camera-based estimated pose byminimizing the distance between the estimated mesh and measured positions. Ourmethod is agnostic to robot configurations. Experiments were conducted usingthe Toyota Human Support Robot, which has a camera, 2D LiDAR, and a touchsensor on the robot arm. Our proposed method demonstrated the superiority inthe human pose estimation accuracy on the quantitative comparison. Furthermore,our proposed method reliably estimated the pose of the target person inpractical settings such as target people occluded by a blanket and standing aidwith the robot arm.

Multimodal Active Measurement for Human Mesh Recovery in Close Proximity