gt-traces

GrandTour (ANYmal legged robot) surface-trace dataset: first-person frames with the future driven path projected into the image, plus every derived label precomputed as columns. One row = one frame; clean and overlay renderings both embedded. All labels come from the robot's own logs and automatic segmentation — no human annotation.

Built from the GrandTour ANYmal missions (hdr_front camera, DLIO map odometry), 48 missions.

Summary

• Total samples: 16,421 (48 missions)
• Named surface transitions: 2,840 across 2,247 rows
• usable (surface labeling gate): 13,202 (80.4%)
• Conditions: 763 snow-covered rows; lighting 14,614 daylight / 1,486 dusk / 321 night

Schema

Field	Type	Notes
mission	string	GrandTour mission name (encodes local start time)
frame_idx	int	per-mission frame index
image	Image	clean hdr_front frame (max side 1024)
image_overlay	Image	same frame with the future driven path drawn
image_width / image_height	int	pixel size (polyline coordinate space)
polyline_xy	List[[x, y]]	driven path projected into the image, pixel coords, ≤60 waypoints at 0.25 m arc steps
surface_seq	List[string]	canonical ground-surface label per waypoint (Mask2Former-COCO-panoptic; same merged vocabulary as adipotnis/ts-polyline-v2, plus metal grate from mission context)
transitions	List[struct]	named surface crossings {from_surface, to_surface, x, y, waypoint_start, waypoint_end}, x/y normalized
offground_frac	float	fraction of waypoints on non-ground classes
usable	bool	offground_frac <= 0.5
z_profile	List[float]	ground height per waypoint from odometry; [] where unavailable
surface_runs	string	JSON [{label, start, end, verb}] — surface runs with walks on / climbs / descends verbs (±0.25 m z change over a run)
mean_luma	float	mean grayscale value of the frame
lighting	string	daylight / dusk / night (mission start hour + luma override)
snowy	bool	any snow waypoint label in the frame

Label provenance

• Path: the robot's actually-driven trajectory (DLIO odometry) — self-labeled.
• Surfaces/transitions: facebook/mask2former-swin-large-coco-panoptic, canonical word-matched vocabulary shared with the wheeled dataset; crossings need ≥3-waypoint runs each side, narrow unknown gaps (≤5 waypoints) bridged.
• Climb/descend verbs: z change across a surface run from the elevation profile.
• Build script: ts-polyline-pipeline/grand_tour/build_gt_traces.py.

Access is manually gated — request access and briefly say what for.

If you use this dataset, please also credit the GrandTour dataset (Fankhauser et al., ETH Zürich Robotic Systems Lab).