Ethgar/hoverdrop-drops-5k

HoverDrop Ballistic Drop Dataset

Simulated point-mass ball drops for the ballistics half of LeDrone Track B (hoverdrop). Each record is one drop: the conditions a release is solved from, the resulting landing, and a top-down camera frame rendered at the release pose.

• 5,000 drops total — Train: 4,000 (16 shards) · Val: 500 (2) · Test: 500 (2)
• Frames: 128x128x3 uint8, one per drop.

How it was generated

A point mass with quadratic air drag in wind-relative air is integrated (semi-implicit Euler, 200 Hz) from a release state down to a flat ground plane. Conditions are randomized per drop: altitude AGL U[5,40] m; 40% exact hover and the rest moving (U[0.5,12] m/s horizontal at a random heading + U[-2,2] m/s vertical); wind U[0,8] m/s horizontal at a random heading + U[-0.5,0.5] vertical. The ball's mass (±10%) and drag coefficient (±20%) are randomized but not recorded in the model inputs — this unobserved variation sets the irreducible landing-error floor. The top-down frame is a nadir pinhole render over a procedural trajplanner terrain map (seed varies per drop). Sim only; no real flight data.

Schema

Compressed .npz shards {split}_{id}.npz, one row per drop, deterministic 80/10/10 split by shard id:

array	shape	dtype	meaning
inputs	(n, 7)	float32	[altitude_agl, vx,vy,vz, wx,wy,wz] (DropNet inputs)
label	(n, 3)	float32	[dx, dy, fall_time] landing offset + fall time
traj	(n, L, 3)	float32	20 Hz ball trajectory (padded)
traj_len	(n,)	int32	valid trajectory length
ball	(n, 3)	float32	TRUE ball params [mass, cd, radius] (unobserved)
drone_state	(n, 18)	float32	release pose the frame is rendered from
target_xyz	(n, 3)	float32	true landing point (drawn disc)
map_seed	(n,)	int32	synthetic terrain seed
frames	(n, 128, 128, 3)	uint8	top-down render at release

Usage

pip install "hoverdrop[hf] @ git+https://github.com/edgarmoreaualix/LeDrone.git#subdirectory=hoverdrop"
python scripts/download_dataset_hf.py --repo-id Ethgar/hoverdrop-drops-5k --out-dir data/drops5k

from hoverdrop.drops import DropDataset
ds = DropDataset("data/drops5k", split="train", load_frames=False)  # DropNet training
x, y = ds[0]        # inputs (7,), label (3,)
rec = DropDataset("data/drops5k", "test", load_frames=True).record(0)  # + frame

License

MIT. Independent clean-room simulation; not affiliated with any third party.