Given a short query with keyphrases about a research idea in 2018 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A new dataset collected in real-world to facilitate the research in DRL based visual navigation.

Keyphrases: robot visual navigation images paragraph-level

Dataset description:
UAVA: The UAVA,<i>UAV-Assistant</i>, dataset is specifically designed for fostering applications which consider UAVs and humans as cooperative agents. We employ a real-world 3D scanned dataset (<a href="https://niessner.github.io/Matterport/">Matterport3D</a>), physically-based rendering, a gamiﬁed simulator for realistic drone navigation trajectory collection, to generate realistic multimodal data both from the user’s exocentric view of the drone, as well as the drone’s egocentric view.
BDD100K: Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities.
DAQUAR: DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images.
ScanNet: ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects.

Options:- A: UAVA  B: BDD100K  C: DAQUAR  D: ScanNet
ScanNet