roverslip op• Data kinds: signal × signal → table
• Call: import fullseye as fs; fs.ledger.slip_gp_fit(slopes, slips, length_scales=None, noise_levels=None) -> 'dict' (to call the implementation directly, import roverslip; roverslip.slip_gp_fit(slopes, slips, length_scales=None, noise_levels=None) -> 'dict'; from the registry, opsdrive.get("slip_gp_fit"))
Gaussian process from slope angle to slip ratio (RBF kernel + homoscedastic observation noise). Hyperparameters by grid search on the log marginal likelihood.
> The detailed description below is the original text — the summary and the headings are translated.
y は平均を引いて標準偏差で割ってから当てはめる(信号の分散 σ_f² = 1 に固定、ℓ と雑音 σ_n を探す)。length_scales の既定は角の範囲の 0.05〜2 倍の 16 段(対数)、noise_levels は 0.01〜1(正規化した y の単位)の 16 段。返り値は予測に使う dict(kind = "gp"、x・alpha・L・ell・noise・y_mean・y_scale・ log_marginal_likelihood)。雑音が角によって変わる(異分散)データでは、帯は全体の平均の幅になる(門の罠)。
• 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.
• poc_rover_slip_risk_path — py -3.11 examples/poc_rover_slip_risk_path.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
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.