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Robotic detection of people in crowded and/or cluttered human-centeredenvironments including hospitals, long-term care, stores and airports ischallenging as people can become occluded by other people or objects, anddeform due to variations in clothing or pose. There can also be loss ofdiscriminative visual features due to poor lighting. In this paper, we presenta novel multimodal person detection architecture to address the mobile robotproblem of person detection under intraclass variations. We present a two-stagetraining approach using 1) a unique pretraining method we define as TemporalInvariant Multimodal Contrastive Learning (TimCLR), and 2) a Multimodal FasterR-CNN (MFRCNN) detector. TimCLR learns person representations that areinvariant under intraclass variations through unsupervised learning. Ourapproach is unique in that it generates image pairs from natural variationswithin multimodal image sequences, in addition to synthetic data augmentation,and contrasts crossmodal features to transfer invariances between differentmodalities. These pretrained features are used by the MFRCNN detector forfinetuning and person detection from RGB-D images. Extensive experimentsvalidate the performance of our DL architecture in both human-centered crowdedand cluttered environments. Results show that our method outperforms existingunimodal and multimodal person detection approaches in terms of detectionaccuracy in detecting people with body occlusions and pose deformations indifferent lighting conditions.

Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations