README.md

metadata

license: other
pretty_name: ts-polyline
tags:
  - robotics
  - navigation
  - terrasentia
  - polyline

ts-polyline

TerraSentia (wheeled robot) navigation trajectories from ROS bags, packaged in a schema mirroring adipotnis/scand-polyline. Each row is one image frame paired with a 60-waypoint future trajectory (next 15 m of EKF odom sampled at 0.25 m steps), projected into the ZED camera as a pixel-space polyline and rendered as an overlay PNG.

Summary

• Total samples: 108491
• Bags: 22
• Frames flagged collision: 57156 (52.7%)
• Frames flagged reversing: 28860 (26.6%)
• Distinct collision events across all bags: 6057

Schema

Field	Type	Notes
traj_id	string	bag stem
robot	string	constant "Wheeled Robot"
run_id	int32	per-bag index
sub_id	int32	0 (schema parity with scand-polyline)
n_frames	int32	total frames in this bag
frame_idx	int32	per-bag frame index
prefix	string	same as traj_id
image	Image	rectified ZED left frame
image_overlay	Image	same frame with projected polyline drawn
depth	Image	nearest ZED registered depth frame, 16-bit PNG in millimeters (0 = invalid). Decode: np.asarray(row['depth']).astype(np.float32) / 1000.0 to get meters.
traversability	Image	nearest /terrasentia/traversability_map frame, mono8 PNG, 336x188 (lower res than RGB).
embodiments	List[string]	["Wheeled Robot"]
polyline_xy	List[List[float64,2]]	projected pixel polyline (in-image segment)
traj	List[List[float64,3]]	up to 60 base-frame waypoints (next 15 m @ 0.25 m)
ground_truth	dict	{"Wheeled Robot": [polyline_xy]}
image_width / image_height	int64	rectified frame dims
collision	bool	EKF speed drop in next 5 s (and not reversing)
reversing	bool	sustained reverse motion in next 5 s

Per-bag top-view maps

Forward motion in green, reverse motion in blue, detected collision events as red dots.

ts_2023_04_10_20h05m59s_filtered

• frames: 4334 · collision frames: 3089 · reversing frames: 1682 · collision events: 320

ts_2023_04_10_20h12m46s_filtered

• frames: 4508 · collision frames: 2687 · reversing frames: 1960 · collision events: 316

ts_2023_04_11_17h47m24s_filtered

• frames: 2276 · collision frames: 1112 · reversing frames: 616 · collision events: 134

ts_2023_04_11_17h51m03s

• frames: 6077 · collision frames: 3277 · reversing frames: 1130 · collision events: 298

ts_2023_04_11_17h53m56s_filtered

• frames: 6478 · collision frames: 3325 · reversing frames: 1727 · collision events: 416

ts_2023_04_11_17h58m32s

• frames: 9979 · collision frames: 4918 · reversing frames: 1840 · collision events: 493

ts_2023_04_11_18h00m20s

• frames: 7276 · collision frames: 3524 · reversing frames: 1855 · collision events: 338

ts_2023_04_11_18h02m48s_filtered

• frames: 6799 · collision frames: 3194 · reversing frames: 1951 · collision events: 414

ts_2023_04_11_18h13m59s

• frames: 2983 · collision frames: 1297 · reversing frames: 537 · collision events: 120

ts_2023_04_11_19h57m53s

• frames: 3693 · collision frames: 1687 · reversing frames: 950 · collision events: 135

ts_2023_04_11_20h02m40s

• frames: 2022 · collision frames: 708 · reversing frames: 484 · collision events: 41

ts_2023_06_25_20h32m20s

• frames: 6417 · collision frames: 3838 · reversing frames: 2181 · collision events: 417

ts_2023_06_25_23h24m19s

• frames: 5280 · collision frames: 2955 · reversing frames: 1463 · collision events: 360

ts_2023_06_25_23h34m37s

• frames: 7269 · collision frames: 3477 · reversing frames: 1706 · collision events: 306

ts_2023_06_25_23h56m10s

• frames: 2050 · collision frames: 1391 · reversing frames: 491 · collision events: 337

ts_2023_06_25_23h56m47s

• frames: 6002 · collision frames: 2645 · reversing frames: 1528 · collision events: 213

ts_2023_06_26_00h04m16s

• frames: 973 · collision frames: 437 · reversing frames: 141 · collision events: 44

ts_2023_06_26_00h05m52s

• frames: 2383 · collision frames: 1319 · reversing frames: 505 · collision events: 153

ts_2023_06_26_00h05m58s

• frames: 4621 · collision frames: 2681 · reversing frames: 1482 · collision events: 245

ts_2023_06_26_00h14m59s

• frames: 1989 · collision frames: 1204 · reversing frames: 564 · collision events: 159

ts_2023_06_26_00h15m21s

• frames: 7259 · collision frames: 3984 · reversing frames: 1992 · collision events: 377

ts_2023_06_26_00h25m04s

• frames: 7823 · collision frames: 4407 · reversing frames: 2075 · collision events: 421

Detection heuristics

All event detection runs on the EKF stream using msg.header.stamp — the rosbag receipt timestamps for /terrasentia/ekf are batched in microseconds and don't reflect the true ~100 Hz sample times. The window is the 5 s of EKF following the frame.

Currently applied (Option B — sharp-decel collision, both flags independent):

• reversing: signed velocity along robot heading is < -0.05 m/s for more than 30 % of segments in the window.
• collision: speed transitions from > 0.5 m/s to < 0.1 m/s within 0.3 s anywhere in the window. The two flags are not mutually exclusive — a bump-then-back-up event fires both.

Alternative considered (Option A — strict, with mutual exclusivity):

• reversing: same as above.
• collision: speed transitions from > 0.5 m/s to < 0.05 m/s within 0.5 s, and stays below 0.05 m/s for at least 1.0 s, and the frame is not also reversing. On TerraSentia teleop this leaves collision=True for the rare truly-stuck events; most bumps were labeled reversing=True only.

Built with datasets/ts_data/bags_to_hf_polyline.py.