When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous

• URL: http://arxiv.org/abs/2006.09959v2
• Date: Tue, 3 Nov 2020 13:57:23 GMT
• Title: When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous
• Authors: Xi Sun, Xinshuo Weng and Kris Kitani
• Abstract summary: We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU) Our proposed method is able to accurately localize a target person with 80.7% accuracy using only 5 seconds of IMU data and video.
• Score: 29.922954461039698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We aim to enable robots to visually localize a target person through the aid of an additional sensing modality -- the target person's 3D inertial measurements. The need for such technology may arise when a robot is to meet person in a crowd for the first time or when an autonomous vehicle must rendezvous with a rider amongst a crowd without knowing the appearance of the person in advance. A person's inertial information can be measured with a wearable device such as a smart-phone and can be shared selectively with an autonomous system during the rendezvous. We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU). The transformation of the two modalities into the joint feature space is learned through the use of a contrastive loss which forces inertial motion features and video motion features generated by the same person to lie close in the joint feature space. To validate our approach, we compose a dataset of over 60,000 video segments of moving people along with wearable IMU data. Our experiments show that our proposed method is able to accurately localize a target person with 80.7% accuracy using only 5 seconds of IMU data and video.

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