| # Task 2 — Visual + IMU |
|
|
| ## Purpose |
|
|
| Synchronized visual-inertial clips for testing joint reasoning over RGB |
| frames, trajectory, and raw gyroscope/accelerometer signals. |
|
|
| ## Files included |
|
|
| ``` |
| task2_visual_imu/ |
| ├── clips/ |
| │ ├── clip_001/ .. clip_007/ |
| │ │ ├── frames/F01.png .. F10.png |
| │ │ ├── trajectory_2d.png |
| │ │ ├── imu.csv |
| │ │ ├── gyro_plot.png |
| │ │ ├── accel_plot.png |
| │ │ ├── frame_imu_alignment.json |
| │ │ └── metadata.json |
| │ └── ... |
| ├── manifest.jsonl |
| └── README.md |
| ``` |
|
|
| 7 clips (target was 8-12; only 7 of the 8 Stage A source windows have |
| real IMU — `S3`, TUM RGB-D `walking_xyz`, is RGB-D-only with no inertial |
| sensor, and is correctly excluded, not padded with fabricated IMU data). |
|
|
| | clip_id | source | sequence | motion_type | |
| |---|---|---|---| |
| | clip_001 | S1 | EuRoC MH_04_difficult | turn | |
| | clip_002 | S2 | EuRoC V1_03_difficult | turn | |
| | clip_003 | S4 | TUM-VI corridor1_512_16 | corridor | |
| | clip_004 | S5 | TUM-VI corridor4_512_16 | straight | |
| | clip_005 | S6 | TUM-VI magistrale1_512_16 | room | |
| | clip_006 | S7 | TUM-VI room1_512_16 | mixed | |
| | clip_007 | S8 | TUM-VI room2_512_16 | rotation | |
| |
| ## How they were generated |
| |
| Time windows and frame sampling are the exact same ones used for the |
| Stage A benchmark's healthy source clips (`configs/extraction_config.yaml` |
| at https://github.com/deansmile/agentic-slam), reused here for |
| consistency — `frames/` and `trajectory_2d.png` are copied from |
| `task3_open_weight_vlm/cases/<Sx>_healthy/` in this same upload (already |
| verified to be the exact frames the original harness used). |
|
|
| `imu.csv` is extracted directly from each source dataset's raw |
| `mav0/imu0/data.csv` (EuRoC/TUM-VI native format), filtered to the same |
| absolute time window as the frames, with timestamps re-based to be |
| relative to the clip's start (`0.0` = first frame). |
|
|
| ## `imu.csv` schema |
|
|
| ``` |
| timestamp_sec,gx,gy,gz,ax,ay,az |
| ``` |
|
|
| - `timestamp_sec`: seconds, **relative to clip start** (not absolute unix |
| time — see `metadata.json`'s `clip_start_timestamp_abs` to recover |
| absolute time if needed). |
| - `gx,gy,gz`: angular velocity, **rad/s** (gyroscope). |
| - `ax,ay,az`: linear acceleration, **m/s²** (accelerometer, includes |
| gravity — not gravity-compensated, matching the raw sensor convention). |
| - Original units and axes are preserved exactly as recorded by the |
| EuRoC/TUM-VI IMU (`ADIS16448`-class MEMS IMU, nominal 200 Hz) — **not |
| rescaled**. Nominal rate is documented in each dataset's own |
| `mav0/imu0/sensor.yaml` (EuRoC) where present; TUM-VI ships the same |
| column format without a separate `sensor.yaml`, same nominal 200 Hz per |
| the TUM-VI paper. `metadata.json`'s `n_imu_samples` gives the actual |
| observed sample count per clip (~2790-2800 for each 14 s window here, |
| consistent with ~200 Hz). |
|
|
| `gyro_plot.png` and `accel_plot.png` are generated directly from the |
| exact `imu.csv` shipped alongside them (not from a separate/different |
| source) — regenerating the plots from `imu.csv` should reproduce them |
| exactly. |
|
|
| `frame_imu_alignment.json` explicitly maps every `F01`-`F10` frame to its |
| nearest IMU sample (by timestamp): `frame_id`, absolute and |
| clip-relative frame timestamp, and the matched IMU row's index and |
| relative timestamp. |
|
|
| ## How to use them with the GitHub code |
|
|
| Frames/trajectory use the same convention as |
| `trajectory_failure_benchmark/` at https://github.com/deansmile/agentic-slam. |
| `imu.csv`'s schema is intentionally simple (flat CSV, documented units) |
| so it can be loaded directly with `pandas.read_csv` / `numpy.loadtxt` |
| without any project-specific code. |
|
|
| ## Which files are model-visible |
|
|
| `frames/`, `trajectory_2d.png`, `imu.csv` (or `gyro_plot.png`/ |
| `accel_plot.png` if you want a visual IMU representation instead of raw |
| numbers) are all model-visible. |
|
|
| ## Which files contain hidden labels |
|
|
| None of these clips have hidden failure labels — this task is about |
| joint visual+inertial reasoning on **healthy** motion, not failure |
| detection (contrast with Task 1/3, which do carry hidden labels in their |
| `metadata.json`/`benchmark_manifest.jsonl`). `metadata.json` here is pure |
| provenance (source sequence, config, timestamps, units), safe to inspect |
| but not meaningful as a "ground truth label" in the failure-detection |
| sense. |
|
|
| ## Known limitations |
|
|
| - Only 7 clips (not 8-12) because only 7 of the 8 Stage A sources have |
| real IMU; `S3` (TUM RGB-D) is excluded rather than backfilled with |
| synthetic/unrelated IMU data. |
| - All 7 clips reuse Stage A's `_healthy` condition windows (no |
| visual-inertial failure cases are included here — Task 2 is about |
| joint visual+IMU reasoning capability, not failure detection). |
| - Accelerometer readings include gravity (not gravity-compensated); if |
| your use case needs gravity removed, that requires the sequence's |
| known initial orientation, which is not provided here. |
|
|
| ## Source / provenance |
|
|
| - Time windows: `/scratch/ds5725/agentic_slam_github/trajectory_failure_benchmark/configs/extraction_config.yaml` |
| - Frames/trajectory: `task3_open_weight_vlm/cases/<Sx>_healthy/` (this upload) |
| - Raw IMU: `/scratch/ds5725/agentic_slam/datasets/{euroc,tum_vi}/<sequence>/mav0/imu0/data.csv` |
|
|
| ## Minimal example workflow |
|
|
| ```python |
| import json |
| import pandas as pd |
| |
| imu = pd.read_csv("clips/clip_006/imu.csv") # S7, TUM-VI room1 |
| align = json.load(open("clips/clip_006/frame_imu_alignment.json")) |
| |
| f05 = next(f for f in align["frames"] if f["frame_id"] == "F05") |
| nearby_imu = imu.iloc[max(0, f05["nearest_imu_row_index"] - 5):f05["nearest_imu_row_index"] + 5] |
| print(nearby_imu) |
| ``` |
|
|
