# 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)
```