iphone_sensor_suite

iPhone Sensor Suite

Use an iPhone as a full sensor suite (LiDAR RGBD, IMU, confidence) for low-cost robot manipulation and autonomous navigation. Stream to Python or ROS2 over WiFi or USB.

📖 Blog: iPhone as a Robot Sensor Suite (work in progress)

iOS App

The iOS streaming app is required to stream sensor data from the iPhone. We are deciding whether to release the iOS source code or provide a free iOS app. Stay tuned!

Overview

The iPhone LiDAR (dToF flash sensor) + RGB camera + IMU replaces multiple traditional robot sensors:

iPhone Sensor	Replaces	Output
LiDAR + RGB + ML	Depth camera (RealSense)	PointCloud2, depth image
LiDAR (middle row)	2D LiDAR (RPLIDAR, Hokuyo)	LaserScan
RGB camera	USB camera	Color image
IMU (accelerometer + gyroscope)	External IMU	IMU data

Architecture

iPhone (iOS App)                          PC / Robot (ROS2)
┌────────────────────┐                   ┌──────────────────────────┐
│ ARKit captures:    │   WiFi / USB      │ Python SDK               │
│  - RGB image       │ ──────────────→   │  - Decode stream         │
│  - LiDAR depth     │   TCP stream      │                          │
│  - IMU data        │                   │ ROS2 Driver              │
│  - Camera params   │                   │  - PointCloud2           │
│  - Camera pose     │                   │  - LaserScan             │
│  - Confidence map  │                   │  - RGB + Depth images    │
└────────────────────┘                   │  - CameraInfo            │
                                         │  - IMU                   │
                                         │  - TF tree               │
                                         │                          │
                                         │ Calibration              │
                                         │  - ArUco marker pose     │
                                         │  - base → camera_link TF │
                                         └──────────────────────────┘

Project Structure

├── sdk/                    # Python client library
├── ros2-driver/            # ROS2 Jazzy package
├── calibration/            # ArUco-based camera-to-robot calibration
└── images/                 # Demo images

Prerequisites

• iPhone: iPhone 12 Pro or newer (with LiDAR) running the iOS streaming app
• Host machine: Ubuntu with ROS2 Jazzy (for ROS2 usage) or any OS with Python 3.10+ (for Python-only usage)
• Network: iPhone and host machine on the same WiFi network (for WiFi mode)
• USB mode (optional): sudo apt install libimobiledevice-utils libusbmuxd-tools (Linux) or brew install libimobiledevice (macOS)

Quick Start

1. iPhone App

1. Install and launch the iOS streaming app on your iPhone Pro
2. The app displays the server IP address on screen — you'll need this for the next steps

2. Python Client (no ROS2 required)

cd sdk
python3 -m venv venv
source venv/bin/activate
pip install -e ".[visualization]"

# View RGB + depth side-by-side
python examples/simple_viewer.py <IPHONE_IP>

# Test protocol v2 data (confidence, IMU)
python examples/test_v2.py <IPHONE_IP>

3. ROS2 Package

Build

# 1. Create virtual environment (--system-site-packages inherits ROS2 Python packages like cv_bridge, rclpy)
cd ros2-driver
python3 -m venv --system-site-packages venv
source venv/bin/activate

# 2. Install Python dependencies
#    numpy<2 is required for compatibility with ROS2 Jazzy's cv_bridge
pip install "numpy<2" -e ../sdk -e .

# 3. Build with colcon
source /opt/ros/jazzy/setup.bash
cd ..  # project root (parent of ros2-driver/)
colcon build --packages-select ros2_driver --symlink-install

Run

You need two terminals — one for the ROS2 node and one for RViz2.

Terminal 1: Start the ROS2 node

export ROS_DOMAIN_ID=50
source /opt/ros/jazzy/setup.bash
source ros2-driver/venv/bin/activate
# WiFi mode: replace with your iPhone's IP from the iOS app
python3 -m ros2_driver.iphone_sensor_node --ros-args -p host:=<IPHONE_IP>

# USB mode: connect iPhone via USB cable (no WiFi needed)
python3 -m ros2_driver.iphone_sensor_node --ros-args -p usb:=true

You should see output like:

[INFO] Connecting to <IPHONE_IP>:8888...
[INFO] Connected! Starting frame loop.
[INFO] First frame: depth=(192, 256), color=(1440, 1920, 3), conf=yes, imu=yes
[INFO] Published 30 frames

Terminal 2: Visualize in RViz2

export ROS_DOMAIN_ID=50
source /opt/ros/jazzy/setup.bash
rviz2 -d ros2-driver/rviz/iphone_sensor.rviz

A pre-configured RViz layout is included that displays the RGB image, point cloud, laser scan, TF tree, and more.

Terminal 2 (alternative): Verify topics

export ROS_DOMAIN_ID=50
source /opt/ros/jazzy/setup.bash
ros2 topic list
ros2 topic hz /color/image_raw

4. Calibrate with ArUco Marker

Print ArUco marker (DICT_6X6_250, ID 3, 3.8cm) and place it with axes aligned to robot base frame.

python3 -m calibration.camera_calibration

See calibration/README.md for detailed instructions.

ROS2 Topics

Topic	Type	Rate	Description
color/image_raw	sensor_msgs/Image	30fps	RGB image (bgr8, 1920x1440)
color/camera_info	sensor_msgs/CameraInfo	30fps	RGB intrinsics
depth/image_rect_raw	sensor_msgs/Image	30fps	Depth (32FC1 meters, 256x192)
depth/camera_info	sensor_msgs/CameraInfo	30fps	Depth intrinsics (scaled from RGB)
aligned_depth_to_color/image_raw	sensor_msgs/Image	~6fps	Depth upscaled to RGB resolution
aligned_depth_to_color/camera_info	sensor_msgs/CameraInfo	~6fps	Same as RGB intrinsics
depth/color/points	sensor_msgs/PointCloud2	~6fps	Colored point cloud
confidence/image_raw	sensor_msgs/Image	30fps	Confidence map (mono8, 0/1/2)
imu	sensor_msgs/Imu	30fps	Accelerometer + gyroscope
scan	sensor_msgs/LaserScan	30fps	2D laser scan from middle row of depth

QoS: All topics use BEST_EFFORT reliability, VOLATILE durability, KEEP_LAST(1). In RViz2, set the display's Reliability Policy to "Best Effort" or you won't see data.

TF tree: world -> camera_link -> camera_color_optical_frame / camera_depth_optical_frame

ROS2 Parameters

Parameter	Default	Description
host	192.168.1.100	iPhone IP address (WiFi mode)
port	8888	TCP port
usb	false	Use USB mode via iproxy (ignores host)
camera_name	camera	Topic/TF prefix
publish_pointcloud	true	Enable PointCloud2
publish_aligned_depth	true	Enable aligned depth to color
publish_confidence	true	Enable confidence map
publish_imu	true	Enable IMU topic
publish_scan	true	Enable LaserScan topic
depth_range_min	0.1	Min depth in meters
depth_range_max	5.0	Max depth in meters
min_confidence	1	Min ARKit confidence for point cloud (0=low, 1=medium, 2=high)

How iPhone LiDAR Works

The iPhone LiDAR is a 3D dToF (direct Time-of-Flight) flash sensor. ARKit processes the raw data through three internal pipelines:

Pipeline	Input	Output	Persistence	We Use It
Depth	LiDAR + RGB + ML	sceneDepth (256x192 depth image)	Per-frame	Yes
Scene Mesh	Many LiDAR frames accumulated	ARMeshAnchor (triangle mesh + classification)	Persistent	Not yet
Body Tracking	RGB + Neural Engine ML	ARBodyAnchor (91 skeleton joints)	Per-frame	Not yet

Currently we only use Pipeline 1 (depth). The depth image is unprojected to a point cloud (all pixels) and sliced into a LaserScan (middle row).