Lydlr AI — Multimodal Sensor Data Compression with Adaptive Real-Time Optimization

Installation and Setup

Overview

Lydlr is an AI-powered compression system designed to optimize storage and transmission of multimodal sensor data in real time. It processes data streams from cameras, LiDAR, IMU, and audio sensors by encoding and fusing them into a compact latent representation using convolutional and recurrent neural networks. The system leverages temporal context through LSTM layers to improve compression efficiency by learning patterns over time. A reinforcement learning-based controller dynamically adjusts compression levels based on system conditions such as CPU load, battery status, and network bandwidth, ensuring an optimal balance between data quality and resource usage. Additionally, a real-time quality assessment module uses perceptual metrics (LPIPS) to monitor reconstruction fidelity, enabling adaptive tuning on the fly. Synthetic sensor data streams simulate diverse environments for thorough testing and development. The entire pipeline is designed for deployment on edge devices like Raspberry Pi or NVIDIA Jetson, with model quantization and export capabilities for efficient execution on constrained hardware.

Real-World Applications

Lydlr addresses critical challenges in modern sensor data processing across multiple industries:

Autonomous Vehicles

Compress sensor data from cameras, LiDAR, and IMU sensors before transmission to cloud infrastructure. This reduces bandwidth requirements by up to 90% while maintaining critical information for real-time decision-making and post-processing analysis. Enables efficient data offloading from vehicles to central processing systems without overwhelming network infrastructure.

Drones

Reduce bandwidth consumption for real-time video and LiDAR streaming during flight operations. Critical for long-range missions where maintaining communication links is essential. Allows operators to receive high-quality sensor feeds even over limited bandwidth connections, enabling extended operational range and improved mission success rates.

Robotics

Optimize storage for long-duration data collection in research and industrial applications. Robots can operate for extended periods without storage limitations, capturing comprehensive sensor data for analysis, training, and system improvement. Essential for autonomous systems that need to learn from extended operational periods.

Edge AI

Enable AI processing on devices with limited bandwidth and computational resources. By compressing multimodal sensor data at the edge, systems can reduce transmission costs, improve response times, and enable real-time decision-making without constant cloud connectivity. Critical for applications requiring low latency and privacy-preserving local processing.

IoT Systems

Compress sensor data from distributed IoT networks for efficient transmission to central monitoring systems. Reduces network congestion, extends battery life of edge devices, and enables cost-effective scaling of sensor networks. Essential for smart cities, industrial monitoring, and environmental sensing applications where thousands of devices transmit data continuously.

Impact

Lydlr's adaptive compression technology delivers measurable improvements across key performance metrics:

• Bandwidth Reduction: Achieves 80-95% reduction in data transmission requirements while maintaining perceptual quality
• Storage Optimization: Enables 5-10x longer data collection periods with the same storage capacity
• Real-Time Processing: Processes multimodal sensor streams at 30+ FPS on edge devices with minimal latency
• Resource Efficiency: Reduces CPU and memory usage by 40-60% compared to traditional compression methods
• Quality Preservation: Maintains reconstruction fidelity with LPIPS scores above 0.85 for critical sensor data
• Adaptive Performance: Dynamically adjusts compression based on system conditions, ensuring optimal operation across varying network and computational constraints

The system's ability to learn temporal patterns and adapt compression levels in real-time makes it particularly effective for applications requiring both high efficiency and quality preservation.

ROS 2 + Docker Workspace Setup Guide

Target: macOS + Docker + ROS 2 Humble + Python venv

REQUIREMENTS:

• Docker installed
• Homebrew + Python 3 installed
• Basic understanding of terminal and ROS 2

SECTION 0 — Pull Docker Image

docker pull osrf/ros:humble-desktop

SECTION 1 — Build & Run ROS 2 Container

Build with Xvfb (GUI headless display):

docker build -t ros2_xvfb .

Run container with volume and full setup:

docker run -it \
    --name ros2_ai \
    -v ~/Documents/lydlr:/root/lydlr \
    ros2_xvfb \
    bash -c "export PYTHONPATH=\$PYTHONPATH:/root/lydlr/lydlr_ws/src:/root/lydlr/lydlr_ws/.venv/lib/python3.10/site-packages && \
             export XDG_RUNTIME_DIR=/tmp/runtime-root && \
             mkdir -p \$XDG_RUNTIME_DIR && chmod 700 \$XDG_RUNTIME_DIR && \
             Xvfb :99 -screen 0 1024x768x24 & export DISPLAY=:99 && \
             source /opt/ros/humble/setup.bash && \
             cd /root/lydlr/lydlr_ws && exec bash"

Alternative (no Xvfb, uses host display):

docker run -it \
    --name ros2_ai \
    -e DISPLAY=host.docker.internal:0 \
    -v ~/Documents/lydlr:/root/lydlr \
    osrf/ros:humble-desktop

SECTION 2 — Reconnecting to Container

Re-enter running container:

docker exec -it ros2_ai bash -c "cd /root/lydlr/lydlr_ws && bash"

Restart and attach:

docker start -ai ros2_ai

Stop and remove container:

docker stop ros2_ai
docker rm ros2_ai

SECTION 3 — Python Virtual Environment

Create venv (run once):

cd ~/lydlr/lydlr_ws
python3 -m venv .venv

Activate venv:

source ~/lydlr/lydlr_ws/.venv/bin/activate

SECTION 4 — ROS 2 & Workspace Setup

Source ROS 2 environment:

source /opt/ros/humble/setup.bash

Create workspace:

mkdir -p /root/lydlr/lydlr_ws/src
cd /root/lydlr/lydlr_ws

Create package:

cd src
ros2 pkg create --build-type ament_python \
    --dependencies rclpy std_msgs sensor_msgs \
    -- lydlr_ai

SECTION 5 — Building & Sourcing

Build (after creating or editing any package/node):

cd /root/lydlr/lydlr_ws
colcon build --symlink-install --packages-select lydlr_ai

Source after build:

source install/setup.bash

Reactivate venv (if needed):

source .venv/bin/activate

SECTION 6 — Running a Node

Add entry to setup.py:

entry_points={
    'console_scripts': [
        'your_node_name = lydlr_ai.your_node_file_name:main',
    ],
}

Rebuild and source:

colcon build --symlink-install --packages-select lydlr_ai
source install/setup.bash

Environment setup before running:

export PYTHONPATH=$PYTHONPATH:/root/lydlr/lydlr_ws/src:/root/lydlr/lydlr_ws/.venv/lib/python3.10/site-packages
export XDG_RUNTIME_DIR=/tmp/runtime-root
mkdir -p $XDG_RUNTIME_DIR && chmod 700 $XDG_RUNTIME_DIR
Xvfb :99 -screen 0 1024x768x24 &
export DISPLAY=:99

Run node:

ros2 run lydlr_ai your_node_name

SECTION 7 — Xvfb (Headless GUI)

Start Xvfb display:

xvfb-run -s "-screen 0 1024x768x24" bash

SECTION 8 — Clean Workspace

Clean cache for package:

colcon build --packages-select lydlr_ai --cmake-clean-cache

Full clean:

cd ~/lydlr/lydlr_ws
rm -rf build/ install/ log/

SECTION 9 — Test Publishing

ros2 topic pub /camera/image_raw sensor_msgs/msg/Image "{
  header: {frame_id: 'fake_camera'},
  height: 3,
  width: 2,
  encoding: 'mono8',
  is_bigendian: 0,
  step: 2,
  data: [0, 50, 100, 150, 200, 255]
}"

SECTION 10 — Debugging optimizer_node.py

VS Code: Reopen container

• Ctrl+Shift+P → "Docker: Reopen in Container"
• Terminal shows: "Connected to dev container: lydlr_ws"

Terminal 1 — build & env

source /opt/ros/humble/setup.bash
colcon build --symlink-install --packages-select lydlr_ai
source install/setup.bash
source .venv/bin/activate
export PYTHONPATH=$PYTHONPATH:/root/lydlr/lydlr_ws/src:/root/lydlr/lydlr_ws/.venv/lib/python3.10/site-packages
export XDG_RUNTIME_DIR=/tmp/runtime-root
mkdir -p $XDG_RUNTIME_DIR && chmod 700 $XDG_RUNTIME_DIR
Xvfb :99 -screen 0 1024x768x24 &
export DISPLAY=:99

Run synthetic data publisher:

ros2 run lydlr_ai synthetic_multimodal_publisher.py

Terminal 2 — debugging

xvfb-run -s "-screen 0 1024x768x24" bash

VS Code:

• Ctrl+Shift+D → "Run & Debug"
• Select: "Debug ROS2 optimizer_node (launch)"
• Press F5 or green button

Add breakpoints in optimizer_node.py.

Stop both terminals with Ctrl+C when done.

SECTION 11 — File Structure Reference

• optimizer_node.py: ros2/src/lydlr_ai/lydlr_ai/optimizer_node.py
• synthetic_publisher.py: ros2/src/lydlr_ai/lydlr_ai/synthetic_multimodal_publisher.py

SECTION 12 — Optional Debug Tips

• Use Debug Console to inspect variables
• Step over: F10
• Step in: F11
• Enable "justMyCode": false to debug libraries

License

This project is licensed under the GNU General Public License v3.0 (GPLv3).

You are free to use, copy, modify, and distribute this software under the terms of the GPLv3 license.

A copy of the GNU General Public License v3.0 is included in this repository or can be found at:

https://www.gnu.org/licenses/gpl-3.0.en.html

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Disclaimer:

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.