File size: 5,656 Bytes
968f82f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | # 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)
```
|