Towards Training Person Detectors for Mobile Robots using Synthetically Generated RGB-D Data = Entrenamiento de detectores de personas para robótica móvil usando conjuntos de datos RGB-D generados sintéticamente
We explore how we can use synthetically generated RGB-D training data from a near photo-realistic game engine to train modality-specific person detectors. We perform ablation studies on a challenging, real-world dataset which we recorded using a Kinect v2 RGB-D sensor in multiple warehouse environments. Through extensive use of domain randomization techniques, we synthesize a realistic and highly varied training set of challenging intralogistics scenarios as observed from a mobile robot, comprising persons in confined and cluttered indoor spaces.
We then train the detector layers of a YOLOv3 model from scratch on our synthetic RGB and jet-encoded depth images. While for the RGB case, we still observe a domain gap of 6 points in mAP compared to a pretrained COCO model, results indicate that by exploiting simulation, an immense manual labeling effort needed to prepare large-scale datasets such as MS COCO might be unnecessary for the depth modality. We further find that filtering of highly occluded groundtruth boundin boxes during training, as well as modeling of time-of-flight sensor noise characteristics has a positive impact on model performance. We also provide an initial set of qualitative results on our real-world dataset.
Fuente:
Biblioteca Virtual Banco de la República
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