> **New to OctoSense?** A [getting-started Colab notebook](https://colab.research.google.com/drive/11hBm5dH--7HueAFCKtaAFnDAJul-52fb?usp=sharing) walks through loading a sequence and using each modality. Click **Open in Colab** above to run it in your browser, no setup required.
**OctoSense** is a time-synchronized, calibrated, multi-sensor dataset spanning multiple platforms, all sharing the same sensor rig. The bulk of the data is large-scale **driving dataset**: **371 sequences · 59 hrs · 2,474 km · 8.43 TB** of urban, suburban, and rural driving (highway / residential / city) on **Long Island** and in **Philadelphia**, across sunrise, daytime, sunset, and nighttime, complemented by additional sequences captured on a **boat** and a **Unitree quadruped**. Every sequence carries synchronized stereo RGB, stereo event cameras, infrared, Ouster LiDAR, and IMU; the driving sequences additionally provide GPS, CAN, ground truth (depth, ego-motion optical flow, semantic segmentation), and a fused GPS + LiDAR-inertial odometry trajectory. Each sequence's recording **`platform`** (`car` / `boat` / `unitree`) is a column in `metadata.parquet`: see [Additional platforms](#additional-platforms--boat--unitree) for the boat/unitree specifics.
> Canonical key for every sequence is its recording datetime, `rosbag2_YYYY_MM_DD-HH_MM_SS`.
> Per-sequence attributes live in **`metadata.parquet`** (the viewer's table: a sensor-montage thumbnail, a GPS-track map, and all metadata columns per sequence).
## Additional platforms — Boat & Unitree
Alongside the 371 **car/driving** sequences, OctoSense includes sequences from two other platforms recorded with the same sensor rig, distinguished by the **`platform`** column in `metadata.parquet` (`car` / `boat` / `unitree`). No perception ground truth is provided for these platforms.
- **`boat/sess1/`: 9 sequences**: No LiDAR odometry or fused trajectory, and the LiDAR is raw rather than motion-compensated: `data.h5` stores the raw Ouster `range`/`signal`/`ir`/`reflectivity` images instead of the deskewed `ouster/range_pcl` point cloud. Note the boat data used a different version `OS1-64` LiDAR.
- **`unitree/sess1/`: 2 sequences** (Unitree Go2-W quadruped). Runs LiDAR-inertial odometry like the driving data (`ouster/range_pcl` + `ouster/odom/*` + `hf_odom`), and adds per-joint robot state under **`/robot`**: `low_state` (motor `q`/`dq`/`tau`/`temperature` (N,20) + foot force + body IMU + battery), `sport` (body pose/velocity/gait), `low_cmd` (motor commands); see [Unitree ROS Msg Data](https://github.com/unitreerobotics/unitree_ros2/tree/eea2719816eb5920a7c7df08cf880a79b2de42f4/cyclonedds_ws/src/unitree/unitree_go/msg). No GPS lock; no fused trajectory.
## Sensors
| Modality | Sensor | Info | Rate |
|---|---|---|---|
| **RGB** (stereo) | 2× FLIR Blackfly S (Sony IMX421) | 1920×1456 | 100 Hz |
| **Event** (stereo) | 2× SilkyEV VGA (Prophesee) | 640×480 | ≈7 MEv/s avg |
| **Thermal** | FLIR A35 | 320×256 | 50 Hz |
| **LiDAR** | Ouster OS1-64 (Rev 7.1) | 64 beams × 2048 | 10 Hz |
| **IMU** | VectorNav VN-100T | Acc/Gyro/Mag/Baro/Temp | 400 Hz |
| **IMU** (in LiDAR) | IAM-20680HT | Acc/Gyro | 100 Hz |
| **GNSS** | u-blox ZED-F9P | RTK (NTRIP) | 5 Hz |
| **Vehicle** | 2021 Mazda CX-5 | CAN Signals | 50–100 Hz |
## Coordinate frames & calibration
All extrinsics are stored per-sequence under `/calib` in the sequence h5, named `A_T_B`: a 4×4 matrix that
maps a point from frame **B** into frame **A** (e.g. `imgl_T_ouster` takes LiDAR points into the
left-RGB frame). Frame abbreviations:
| Abbreviation | Frame | | Abbreviation | Frame |
|---|---|---|---|---|
| `imgl` / `imgr` | RGB left / right | | `ir` | Infrared (FLIR A35) |
| `evl` / `evr` | Event left / right | | `ouster` | LiDAR |
| `imu` | VectorNav IMU | | | |
Provided extrinsics: `imgl_T_imgr`, `imgl_T_imu`, `imgl_T_ir`, `imgr_T_imu`, `ir_T_imgl`, the event-pair
set (`evl_T_evr`, `evl_T_imgl`, …), `ouster/imgl_T_ouster` / `ouster/ir_T_ouster`, and
`calib/lidar_T_lidarimu` (LiDAR IMU → LiDAR/sensor frame, from the Ouster factory
`imu_to_sensor_transform`). Each sequence records which calibration it used via `rgb_cal_id` /
`imu_cal_id` / `lidar_cal_id` (in the metadata). RKO-LIO odometry (`ouster/odom/*`) tracks the LiDAR pose in the **Map frame**;
the `fused_traj` trajectory tracks the same LiDAR pose in the **UTM-relative world frame** (see Ground truth). The **depth**
GT is rendered in the **rectified left-RGB camera image** (OpenCV `stereoRectify`, `rect_T_raw` applied).
> **Per-session calibration** is released alongside the data, in **Kalibr** format:
> - **Cameras** (`calibration-camchain.yaml`), a **4-camera Kalibr chain**: `cam0` = RGB right
> (`/flir_cam_right`), `cam1` = RGB left (`/flir_cam_left`), `cam2` = event right (`/event_camera_right`,
> 640×480), `cam3` = event left (`/event_camera_left`, 640×480). Each entry has pinhole
> `intrinsics [fx,fy,cx,cy]` + radtan `distortion_coeffs`; each cam after `cam0` carries `T_cn_cnm1`
> (transform from the previous cam in the chain).
> - **Camera↔IMU** (`calibration-camchain-imucam.yaml`, adds `T_img[l/r]_imu`) + the **IMU** noise model (`calibration-imu.yaml`).
> - **LiDAR↔camera** (`lidar_calibration_results.yaml`) and **IR** intrinsics + extrinsics (`ir_calib_result.json`).
>
> The processed h5 renames `cam0`/`cam1` to the `right`/`left` RGB streams above (and `cam2`/`cam3` are the right/left event cameras).
## Ground truth
- **Odometry**: [**RKO-LIO**](https://docs.ros.org/en/jazzy/p/rko_lio/) LiDAR-inertial odometry giving the SE(3) LiDAR pose in the **Map frame** (the LiDAR first frame) with linear/angular velocity. The high-rate `hf_odom/*` is obtained by integrating the IMU between LiDAR keyframes.
- **Depth**: sparse metric depth in the rectified left-RGB image (`depth_cm`, uint16 cm,
`0 = invalid`). Built by accumulating **61 LiDAR scans (≈6 s)**, removing dynamic objects (**YOLO26-medium**
on the nearest RGB frame), keeping the **minimum** depth per pixel, and then projecting into the camera image.
- **Optical flow**: **derived, not stored**: the ego-motion-induced flow (accumulated points projected into a
future RGB frame). Regenerate from `depth + poses` with `derive_flow.py`.
- **Semantic segmentation**: [19-class Cityscapes](https://github.com/mcordts/cityscapesScripts/blob/0bcc4a72549590fd80d71d8465be3a0c4b1c06e8/cityscapesscripts/helpers/labels.py#L62) pseudo-labels ([**EoMT**](https://github.com/tue-mps/eomt)), on the **303 daytime sequences**
(`has_seg = true`); nighttime sequences have no labels.
- **Fused reference trajectory**: RKO-LIO odometry + GPS fused in a pose graph, under **`fused_traj`**
(`T_world_lidar` (N,4,4) + `t`) in a **UTM-relative world frame** anchored at the first GPS fix.
Geo-referencing + fusion quality live as `fused_traj` group attrs (`epsg`/`utm_zone`/`hemisphere`, `origin_{easting,northing,alt}_m`, `lever_xyz_lidar_m`, GPS residual
RMS + p90, `confidence_tier`). `fused_traj/t` is **non-uniformly sampled**: poses are emitted at the union of the LiDAR-odom (≈10 Hz) and
GPS (≈5 Hz) timestamps (≈13 Hz combined), so inter-pose intervals vary (≈0.02–0.1 s).
## Per-sequence files (`///`)
| file | contents |
|---|---|
| `data.h5` | timestamps, IMU, GPS, CAN, LiDAR (range [deskewed by RKO] / sig / nir / refl), RKO-LIO odom, `fused_traj` (fused GPS+LIO trajectory), `/calib` |
| `events.h5` | raw asynchronous event streams (`ev/left`, `ev/right`), **≈78% of a sequence's bytes** |
| `img_{left,right,infrared}.mp4` | per-camera H.265 encoded video |
| `rgb_left_rect_depth.h5` | sparse metric depth in the rectified left-RGB image (`depth_cm`); flow derived via `derive_flow.py` |
| `rgb_left_rect_semantic.h5` | 19-class Cityscapes pseudo-label seg in the rectified left-RGB image, **day sequences only** |
| `captions.h5` | per-window scene captions (Gemma-4-31B VLM) + 4096-d Qwen3-Embedding-8B caption vectors + window metadata |
**Units/conventions:** depth in cm (`0`=invalid); event timestamps in µs; all other h5 time arrays in seconds.
> **Experimental, `car/radar` (Mazda forward radar).** Six object tracks decoded from the CAN data
> with the [opendbc Mazda radar DBC](https://github.com/commaai/opendbc/blob/master/opendbc/dbc/mazda_radar.dbc) (CAN IDs 865–870 = `RADAR_TRACK_361..366`). Layout:
> `car/radar/track_{1..6}/` with `t` (M,) float64 main-clock seconds, `dist` (M,) uint16, `ang` (M,) int16,
> `vrel` (M,) int16. Values are stored **raw** (no scale/offset): `dist`=`DIST_OBJ` (sentinel `4095` = no track),
> `ang`=`ANG_OBJ` (signed 12-bit), `vrel`=`RELV_OBJ` (signed 11-bit).
## Data format
All `*/t` arrays are **seconds on a common PPS-synced main clock** (Kalman-smoothed); event times are
**µs** on that same clock (`t/1e6` → s). Align streams **by timestamp**: RGB runs
≈100 Hz vs LiDAR ≈10 Hz. **Camera frames are raw (un-rectified)**: rectify with the per-camera `intrinsics` +
radtan `dist_coeffs` (stored in the h5 and the camchain); the depth GT is already in the rectified left-RGB frame.
**Main bag h5 (`data.h5`)**:
| key | shape · dtype | meaning |
|---|---|---|
| `ouster/range_pcl` | (N,131072,3) int32 | LiDAR **XYZ in mm**, 64×2048 destaggered (`[0,0,0]` = no return) |
| `ouster/{sig,nir}_pcl` · `refl_pcl` | (N,131072) uint16 · uint8 | per-point signal / near-IR / reflectivity |
| `ouster/odom/map_T_lidart` | (N,4,4) f64 | RKO-LIO LiDAR pose in the **map frame** (translation **m**) (+ `lin_vel` m/s, `ang_vel` rad/s, `t` s); `hf_odom/*` = high-rate |
| `ouster/{accel,ang_vel}` | (M,3) f32 | in-LiDAR IMU (IAM-20680HT); extrinsic to sensor frame = `calib/lidar_T_lidarimu` |
| `img/{left,right}/`, `infrared/` |, | `t` (≈100/100/50 Hz) · `intrinsics` (3,3) · `dist_coeffs` (4,) · `resolution` (2,) |
| `vectornav/` | (K,·) | `accel` (m/s²), `ang_vel` (rad/s), each + `_raw` (uncompensated); `magnetic` (Gauss), `pressure` (Pa), `temperature` (°C), `t` (s); 400 Hz |
| `gps/data` | (G,7) f64 | `[lat°, lon°, alt_m, cov_xx, cov_yy, cov_zz, fix]`. `fix` = ROS `NavSatStatus.status`: **−1** no-fix · **0** standard fix · **1** SBAS · **2** GBAS/RTK. |
| `gps/velocity_enu` | (G,3) f64 | ENU velocity m/s `[East, North, Up]` (from `/fix_velocity`) |
| `car/` | (·,2+) f32 | CAN (col 0 = time, s): `speed` (mph), `wheels` (4× wheel speed, mph), `steer` (deg), `steer_rate` (deg/s), `vcc` = `[acc_x, acc_y]` (≈m/s²), `brake_on` (0/1), `pedal` & `brake_press` (raw counts) + `radar/` (above). **Experimental, decoded via a community Mazda DBC; units approximate/unverified.** |
| `fused_traj/T_world_lidar` | (R,4,4) f64 | **fused** RKO-LIO+GPS pose, **UTM-relative world frame** (+ `fused_traj/t`; geo-ref + quality in `fused_traj` attrs: `epsg`/`utm_zone`, `origin_*`, `confidence_tier`, residual RMS/p90) |
| `calib/` |, | extrinsics `A_T_B` |
**Events (`events.h5`)**: flat asynchronous event streams per side, plus the **event-camera calibration**:
| key | dtype | meaning |
|---|---|---|
| `ev/{left,right}/t` | uint64 | event time **µs** (main clock) |
| `ev/{left,right}/{x,y}` | uint16 | pixel col / row, **raw 640×480** (un-rectified) |
| `ev/{left,right}/p` | uint8 | polarity (0/1) |
| `ev/{left,right}/ms_to_idx` | uint64 | millisecond → event index |
| `ev/{left,right}/intrinsics` | (3,3) f64 | pinhole `K` at native **640×480** |
| `ev/{left,right}/dist_coeffs` | (4,) f64 | radtan `[k1, k2, p1, p2]` |
| `ev/{left,right}/resolution` | (2,) int32 | `[width, height]` = `[640, 480]` |
**Depth GT (`rgb_left_rect_depth.h5`)**: per-LiDAR-scan sparse depth in the **rectified left-RGB** image.
`N` = `n_lidar_frames`; frame `k` ↔ LiDAR scan `k`. Carries its own `timestamps`
(identical to `data.h5` `ouster/t`) and index arrays:
| key | shape · dtype | meaning |
|---|---|---|
| `depth_cm` | (N,1456,1920) uint16 | metric depth in **cm** (`m = depth_cm/100`), `0 = invalid` |
| `timestamps` | (N,) f64 | main-clock seconds (= `data.h5` `ouster/t`) |
| `lidar_indices` | (N,) int64 | LiDAR-scan index (`0..N−1`) |
| `left_img_indices` | (N,) int64 | matching frame in `img_left.mp4`|
| `poses` | (N,4,4) f32 | per-scan LiDAR pose used to accumulate the depth (input to `derive_flow.py`) |
| `K_rect` · `R3` | (3,3) f64 | rectified left-RGB intrinsics · rectification rotation `rect_T_raw` (OpenCV `R1`) |
| `imgl_T_ouster` | (4,4) f64 | LiDAR → **raw (unrectified)** left-RGB cam |
| `raw_res` | (2,) int32 | source image resolution |
Root attrs: `depth_scale_cm`, `flow_{gap,scale,invalid}`, `flow_stored` (=`False`: flow is **derived, not stored**), `n_scans`.
**Semantic GT (`rgb_left_rect_semantic.h5`, day sequences only)**: per-LiDAR-scan 19-class seg in the
**rectified left-RGB** image, aligned 1:1 with `rgb_left_rect_depth.h5`:
| key | shape · dtype | meaning |
|---|---|---|
| `semantic` | (N,1456,1920) uint8 | Cityscapes class id `0..18` (see `classes` attr); `255` = ignore |
| `lidar_indices` | (N,) int64 | LiDAR-scan index for each frame (`0..N−1`) |
| `left_img_indices` | (N,) int64 | matching frame in `img_left.mp4` |
| `timestamps` | (N,) f64 | main-clock seconds |
Root attrs: `classes` (19 names), `num_classes=19`, `model=EoMT-Cityscapes-DINOv2-L-1024`,
`coordinate_frame=rectified_left`, `preprocessing=rectify+CLAHE`, `resolution=1920x1456`.
So `semantic[k]` ↔ `depth_cm[k]` ↔ `img_left.mp4[left_img_indices[k]]` ↔ `ouster/t[k]`.
**Captions & semantic search (`captions.h5`)**: each sequence is split into **≈5 s windows** (`W` per
sequence ≈ duration / 5 s); every window gets a natural-language scene caption (a **Gemma-4-31B** VLM) and a
4096-d text embedding (**Qwen3-Embedding-8B**) for text retrieval:
| key | shape · dtype | meaning |
|---|---|---|
| `captions` | (W,) str | one scene caption per window |
| `embeddings` | (W,4096) f32 | Qwen3-Embedding-8B vector for each caption |
| `metadata` | (W,) struct | `window_id`, `frame_idx` (→ `img_left.mp4` frame), `timestamp` (main-clock s), `speed_mps`, `turn_deg`, `dist_m`, `is_night` |
Root attrs: `caption_model`, `embed_model`, `embed_dim` (4096), `num_windows`, `video_id`.
## Splits
`train` = 293 · `test` = 78. The dataset contains daytime, nighttime, and degraded sequences.
## `metadata.parquet` fields (28)
`platform` ∈ {`car`, `boat`, `unitree`}, the recording platform (see Additional platforms). The remaining fields:
`bag_id, session, start_time, split, is_daytime, degraded, has_seg,
duration_s, n_lidar_frames, n_rgb_frames, n_imu_samples, n_events_left, n_events_right,
n_gps_fixes, n_gps_valid, gps_quality, gps_lat_min/max, gps_lon_min/max,
mean_speed_mph, idle_fraction, distance_m, rgb_cal_id, imu_cal_id, lidar_cal_id, sensor_dropout`
- `gps_quality` ∈ {`RTK_fixed_cm`, `float_dm`, `single_m`, `no_fix`, `absent`}, 67 / 62 / 239 / 2 / 1 (RTK-fixed **≈1–3 cm**, float **≈0.1–0.5 m**, single **≈0.5–3 m**).
- `sensor_dropout`: `null`, or `sensor:seconds[;sensor:seconds]`
## Download
The full dataset is 8+ TB. The
[`download_octosense.py`](https://github.com/anthonytec2/OctoSense/blob/main/download_octosense.py)
helper (in the GitHub repo) selects by platform, sequence, and modality, and can skip the raw
event streams (`events.h5`, ~78% of each sequence's bytes):
```bash
pip install huggingface_hub
# all car sequences (everything)
python download_octosense.py --platform car --out ./octosense
# the same, but skip the raw event streams (events.h5, ~78% of the bytes)
python download_octosense.py --platform car --no-events
# one sequence, only the core LiDAR/IMU/GPS h5 + depth + segmentation
python download_octosense.py --sequence rosbag2_2026_01_04-13_51_24 --modalities data,depth,seg
```
`--modalities` picks from `data, events, captions, depth, seg, rgb, ir`.
Or pull files directly with `huggingface_hub`:
```python
from huggingface_hub import snapshot_download
snapshot_download("anthonytec2/OctoSense", repo_type="dataset",
allow_patterns="car/sess7/**") # one session
```
## Loading
```python
import h5py, hdf5plugin, numpy as np, pyarrow.parquet as pq
meta = pq.read_table("metadata.parquet").to_pydict() # per-sequence table
with h5py.File("///data.h5") as f:
lidar = f["ouster/range_pcl"][:]
odom = f["ouster/odom/map_T_lidart"][:]
# RGB / IR frames, native-res H.265 mp4 decoded with torchcodec.
from torchcodec.decoders import VideoDecoder
dec = VideoDecoder("///img_left.mp4") # 1920x1456; len(dec) == n_rgb_frames
rgb_k = dec[k] # (3, H, W) uint8 tensor at frame k
# RGB runs ≈100 Hz vs LiDAR ≈10 Hz, align by TIMESTAMP, not shared index:
with h5py.File("///data.h5") as f:
img_t = f["img/left/t"][:] # seconds, same main clock
lid_t = f["ouster/t"][:]
k = int(np.argmin(np.abs(img_t - lid_t[j]))) # RGB frame nearest LiDAR frame j
# img_right.mp4 / img_infrared.mp4 decode the same way (IR is grayscale-as-video, ≈50 Hz).
# Depth + semantic GT, one frame per LiDAR scan, in the rectified left-RGB image:
with h5py.File("///rgb_left_rect_depth.h5") as f:
depth_m = f["depth_cm"][j] / 100.0 # (1456,1920); 0 = invalid (j = LiDAR scan)
with h5py.File("///rgb_left_rect_semantic.h5") as f: # day sequences only
seg = f["semantic"][j] # (1456,1920) uint8, class 0..18
rgb_idx = f["left_img_indices"][j] # matching img_left.mp4 frame for scan j
# flow is derived, not stored — for LiDAR scan j, pass odom as poses (map_T_lidart).
# Flow is from scan j -> j+gap; gap is read from the depth h5 attr `flow_gap` (default 2).
from derive_flow import derive_flow_from_h5
flow_i16 = derive_flow_from_h5("///rgb_left_rect_depth.h5", j, poses=odom)
flow_uv = flow_i16.astype(np.float32) / 8.0 # (2,H,W) px (invalid where flow_i16 == -32768)
```
## Statistics
**382 sequences across 3 platforms:**
- **car**: 371 sequences · 59 hrs · 2,474 km · 8.43 TB · 303 day / 68 night · 4 degraded · segmentation on 303 · GPS: 67 RTK / 62 float / 239 single / 2 no-fix / 1 absent.
- **boat**: 9 sequences · ≈1.2 hrs · GPS: 8 RTK / 1 absent
- **unitree**: 2 sequences · ≈15 min · ≈0.8 km traversed · quadruped joint state
## Notes/Known limitations
- **Optical flow is derived solely from ego-motion**: rigid camera-motion reprojection of LiDAR depth; it does **not**
capture independent motion of dynamic objects.
- **Seg is day-only** (303 sequences); nighttime sequences have no segmentation labels.
- **No color correction was performed** on the RGB frames, so colors can look raw or cast. You can apply a [gray-world](https://en.wikipedia.org/wiki/Color_normalization#Grey_world) white-balance correction if you need more natural-looking colors.
- **IMU compensated vs raw.** VectorNav factory-calibrates each unit (bias/scale/axis-misalignment + temperature applied). In this dataset `accel` ≡ `accel_raw`; `ang_vel` additionally has the on-board EKF's real-time gyro-bias estimate removed.
- **5 sequences have a >10 s sensor dropout** (see `sensor_dropout`): 3 with the event camera off early
(20–43 s), 1 GPS+CAN, 1 CAN. **3 sequences have no usable GPS** (`no_fix`/`absent`), and **5 sequences have no `fused_traj`** (GPS too sparse / low-quality for the pose-graph fusion).
- **IR & GPS are PPS-synced indirectly, through the IMU PPS-system clock calibration.**
- **IR calibration is best-effort.** The infrared intrinsics/extrinsics were calibrated from existing data without a heated target board. They are estimated from the markerboard's 4 corners alone, then manually refined. Treat the IR calibration as approximate.
- **LiDAR clock is PPS-stepped, not Kalman-aligned.** Unlike the other streams, the Ouster clock is not
actively Kalman-smoothed onto the main clock; its internal clock only advances its whole-second
counter when the PPS edge arrives.
- **IMU is noisy from vehicle vibration.** Both IMUs pick up road and engine vibration; the VectorNav is
soft-mounted to dampen it, but residual vibration noise remains in the accelerometer / gyro signals.
- **IR frame rate occasionally dips below 50 Hz.** The infrared camera is not run as a composable node, so
under load it sometimes falls below its nominal 50 Hz capture rate (gaps visible in `infrared/t`).
- **RKO-LIO cold-start transient.** The estimator's initial phase can occasionally be jerky, at the start of a
sequence the LIO briefly reports ≈zero motion, then "catches up" with a jump once it converges →
jerky roll/pitch (occasionally z) over the first few meters of motion (upstream [RKO-LIO issue #139](https://github.com/PRBonn/rko_lio/issues/139)).
- **LiDAR deskew is a constant-motion approximation.** Each ~100 ms sweep is
[motion-compensated by RKO-LIO](https://github.com/PRBonn/rko_lio/blob/a67dd406caaa8229d3ff858f2f38519eb8097831/rko_lio/core/lio.cpp#L437)
using the **average** body acceleration `a` and **average** angular velocity `ω` over the sweep, a point at offset
`dt` from the scan reference time is warped by `exp([ v·dt + ½·a·dt², ω·dt ])` (constant-acceleration
translation + constant-angular-velocity rotation). Because `a`/`ω` are held constant across the sweep,
rapid intra-sweep motion (high jerk, sharp turns, potholes) leaves some residual skew; `a` is
Kalman-filtered + jerk-bounded to limit this but cannot fully remove it.
- **LiDAR noise can leak into the depth GT.** The depth ground truth is projected directly from the raw
Ouster returns, so sensor noise (stray or spurious returns from rain, snow, fog, dust, retroreflectors
(see [LiDAR ghosts & blooming](https://www.teachkidsrobotics.com/blog/what-are-lidar-ghosts-and-blooming/)),
or specular/multi-path reflections) can survive into our depth ground truth as a small number of
erroneous points.
- **Platform Bounce.** On a few sequences the SeaSuckers loosened, resulting in vertical motion (bounce) of the platform.
- **Raw ROS 2 bags are currently unreleased.** If you require the original ROS 2 bag recordings, feel free to contact [pratikac@upenn.edu](mailto:pratikac@upenn.edu).
We have taken great care to perform data quality checks on this data. That said, some issues at this scale may slip through, so should you find any examples of gross desynchronization, please report them and we can take a look.
## Citation
```bibtex
@misc{bisulco2026octosense,
title = {{OctoSense}: Self-Supervised Learning for Multimodal Robot Perception},
author = {Bisulco, Anthony and Wang, Jeremy and Daniilidis, Kostas and Balestriero, Randall and Chaudhari, Pratik},
year = {2026},
howpublished = {Preprint},
}
```
## License
Released under the **MIT License**: free to use, modify, and redistribute with attribution; provided "as is" without warranty. If you use OctoSense, please cite the paper (see Citation above).
