Given a short query with keyphrases about a research idea in 2019 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: I want to compare classic and learning-based navigation systems.

Keyphrases: navigation images, maps, laser range data

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.
Matterport3D: The Matterport3D dataset is a large RGB-D dataset for scene understanding in indoor environments. It contains 10,800 panoramic views inside 90 real building-scale scenes, constructed from 194,400 RGB-D images.
HoME: HoME (Household Multimodal Environment) is a multimodal environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer.
MusicNet: MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping.

Options:- A: UAVA  B: Matterport3D  C: HoME  D: MusicNet
Matterport3D