ts-polyline-v2

adipotnis/ts-polyline with every derived label baked in as columns — no sidecar files, no training-time joins. One row = one camera frame with its future driven trajectory and all supervision signals side by side.

TerraSentia (wheeled field robot) navigation from 22 ROS bags. Per frame: the next 15 m of EKF odometry sampled at 0.25 m steps (up to 60 waypoints), projected into the ZED camera as a pixel-space polyline and rendered as an overlay. Every label is derived from the robot's own logs — no human annotation.

Summary

• Total samples: 108,491 (22 bags)
• collision: 16,372 (15.1%) · reversing: 26,300 (24.2%)
• Named surface transitions: 9,655 across 8,269 rows
• usable (surface labeling gate): 71,050 (65.5%); 10,793 rows geometry-rejected (polyline projects into an obstacle within 1 m — wall-projection artifacts)
• Wheel-slip coverage: 99% of frames scoreable; traction mean 90.3 (p10 82, p90 98, 0–100 scale)
• Object descriptions: 20,000 frames

Schema

All v1 columns are unchanged (image, image_overlay, depth [16-bit PNG, mm], traversability, polyline_xy, traj, collision, time_to_collision, reversing, ids). New columns:

Field	Type	Notes
surface_seq	List[string]	canonical ground-surface label per waypoint (Mask2Former-COCO-panoptic, synonym-merged vocabulary: pavement/sidewalk/path/road are ONE class; offground = non-ground)
transitions	List[struct]	named surface crossings along the path: {from_surface, to_surface, x, y, waypoint_start, waypoint_end}, x/y normalized image coords
offground_frac	float	fraction of waypoints on non-ground classes
geom_ok	bool	False = the drawn path enters an obstacle within 1 m arc (depth-walk test) — projection artifact, exclude from path tasks
collision_arc_m	float	arc length to the first waypoint whose scene depth is shorter than its forward range; −1 if the path is clear
collision_point	List[float]	[x, y] normalized coords of the blocking pixel; [] if clear
usable	bool	geom_ok and offground_frac <= 0.5
slip_seq	List[float]	wheel slip in [0,1] at the moment the robot actually crossed each waypoint: `1 − v_EKF / mean(
objects	string	JSON list of visible objects {label, cx, cy, bin} (normalized center, near/mid/far distance band) from SAM3 detections + depth; "" where not computed

Label provenance

• Surfaces/transitions: facebook/mask2former-swin-large-coco-panoptic semantic segmentation, canonical word-matched vocabulary; crossings need ≥3-waypoint runs each side, narrow unknown gaps (≤5 waypoints) bridged.
• Collision geometry: per-waypoint comparison of measured scene depth vs the waypoint's robot-frame forward range.
• Slip: two independent logs (wheel encoders vs EKF body speed) sampled at each waypoint's interpolated traversal time — experienced traction, complementing the camera-predicted traversability map.
• Build scripts: ts-polyline-pipeline (scripts/build_ts_polyline_v2.py and the sidecar builders it consumes).

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