kalman_ca — DRIVE `balltrack` op

• Data kinds: points → table

• Call: import fullseye as fs; fs.ledger.kalman_ca(z, dt: 'float', *, q: 'float' = 1.0, r: 'float' = 1.0, x0=None, P0: 'float' = 1000.0) -> 'dict' (to call the implementation directly, import balltrack; balltrack.kalman_ca(z, dt: 'float', *, q: 'float' = 1.0, r: 'float' = 1.0, x0=None, P0: 'float' = 1000.0) -> 'dict'; from the registry, opsdrive.get("kalman_ca"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

等加速度モデルの Kalman フィルタ(位置の観測 (N, D)、状態 = 位置・速度・加速度 × D)。

返り値 `{"x" (N, 3D) 更新後, "x_pred" (N, 3D) 予測, "P" (N, 3D, 3D), "innovation" (N, D)}`。

`q = 加速度の変化の分散(白色ジャーク)、r` = 観測の分散。放物線の真値を入れると(モデルが厳密なので)収束後の

新息 → 0、r → 0 で更新後の位置 = 観測(門)。NaN の観測は予測だけ(欠測)。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_ball_bounce — py -3.11 examples/poc_ball_bounce.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`balltrack`)

ball_detect · ball_track · triangulate_dlt · track_triangulate · bounce_detect · marker_direction · spin_from_markers · spin_from_marker_sequence

*Provenance: balltrack.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

kalman_ca — DRIVE balltrack op