OmniInstrument/SLAM_project

Omni Instrument SLAM Project Dataset

The Omni Instrument SLAM Project Dataset is a compact robotics dataset designed for evaluating stereo, visual-inertial, and visual-inertial odometry (VIO) pipelines.

It provides:

• Stereo Image Pairs
• Inertial measurements (IMU)
• Ground-truth 6 DoF pose (for VIO)
• Raw ROS 1 and ROS 2 recordings

Overview

The dataset is structured into three splits:

Split	Description
stereo	Stereo-only (IMU stationary)
stereoinertial	Stereo + IMU
vio	Stereo + IMU + ground-truth pose

All splits share the same schema, enabling consistent downstream pipelines.

Data Collection Protocol

AprilTag grid settings (used for both Stereo and Stereo-Inertial calibration sequences):

Setting	Value
Tag size	20 mm
Tag spacing	6 mm
Tag family/dictionary	tag36h11 (6x6)

1. Stereo (Calibration - Static Sensor)

   • Setup: robot stationary, camera + IMU fixed, AprilTag grid moves
   • Used for: stereo calibration (intrinsics/extrinsics)

   

2. Stereo-Inertial (Calibration - Moving Sensor)

   • Setup: robot moves, camera + IMU move together, AprilTag grid stationary
   • Used for: camera-IMU extrinsics + sync checks

   

3. VIO (Operational SLAM Sequence)

   • Setup: robot moves in a normal environment (no calibration targets)
   • Logged: stereo images, IMU, ground-truth odometry
   • Used for: VIO/SLAM evaluation

   

Data Format

Each example follows the same top-level schema. Some fields are split-dependent:

• stereo: images + timestamp (IMU is stationary; pose not provided)
• stereoinertial: adds IMU (gyro, accel) and time offsets (sync_dt)
• vio: adds ground-truth pose (position, orientation)

Example record:

{
  "image_left": Image,
  "image_right": Image,
  "timestamp": float,

  "gyro": [wx, wy, wz],
  "accel": [ax, ay, az],

  "sync_dt": [dt_right, dt_imu],

  "position": [x, y, z],
  "orientation": [qx, qy, qz, qw]
}

Notes:

• gyro is in rad/s and accel is in m/s^2.
• sync_dt = [dt_right, dt_imu] are time offsets (in seconds) relative to timestamp (left image):
  • dt_right = abs(t_right - t_left)
  • dt_imu = abs(t_imu - t_left)

Sampling Methodology

Each split contains 100 randomly sampled, synchronized frames:

• Uniform sampling across the trajectory
• Start/end trimmed to remove initialization artifacts

Synchronization constraints:

• abs(t_left - t_right) <= 5 ms
• abs(t_left - t_imu) <= 5 ms
• abs(t_left - t_odom) <= 5 ms (VIO only)

Missing Data Handling

For splits without ground truth (stereo, stereoinertial):

position    = [inf, inf, inf]
orientation = [inf, inf, inf, inf]

ROS Topics

Recordings:

• ROS 1 bags: stereo, stereointertial
• ROS 2 MCAP: vio

ROS 1 (Calibration)

/stereo/left/color/image_raw
/stereo/right/color/image_raw
/imu/data

ROS 2 (VIO)

/stereo/left/color/image_raw
/stereo/right/color/image_raw
/imu/data
/ground_truth/odom
/tf

Example Usage

from datasets import load_dataset
import numpy as np

ds = load_dataset("OmniInstrument/SLAM_project", split="vio")
sample = ds[0]

img_l = sample["image_left"]
img_r = sample["image_right"]

gyro = sample["gyro"]
accel = sample["accel"]

pos = sample["position"]
quat = sample["orientation"]

if not np.isinf(np.asarray(pos)).any():
    print("Ground truth available")

License

This software and dataset are released under the MIT License.