slip_predict — DRIVE roverslip op

• Data kinds: table × signal → table

• Call: import fullseye as fs; fs.ledger.slip_predict(model, slopes, level: 'float' = 0.9) -> 'dict' (to call the implementation directly, import roverslip; roverslip.slip_predict(model, slopes, level: 'float' = 0.9) -> 'dict'; from the registry, opsdrive.get("slip_predict"))

Usage

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

滑り率の予測と両側 `level` の帯。ガウス過程は観測の予測分布(潜在の分散 + 雑音)、分位点回帰は曲線そのもの。

返り値 `{"mean", "std", "lower", "upper", "median", "level"}(各 (n,))。分位点回帰の mean` は当てはめた

分位点の平均、`std` は四分位範囲 / 1.349、帯の端は (1 ± level)/2 の分位点(当てはめた段に無ければ ValueError)に、

共形の補正(:func:slip_quantile_fit の `conformal`)があればその幅を足す。

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_rover_slip_risk_path — py -3.11 examples/poc_rover_slip_risk_path.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 (roverslip)

bekker_pressure · bekker_wheel_sinkage · wheel_forces · wheel_sinkage · wheel_traction_curve · slope_slip_curve · ground_shift_track · odometry_slip


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

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.