Unitree G1 LiDAR, SLAM, navigation and control. Dev w/ G1 Humanoid P.2

2025-04-30 · Source: sentdex · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

Significant software upgrades have been implemented for the Unitree G1 humanoid robot, "Jeff," leveraging LLMs like Codecs and 03 for development. The core enhancements include integrating a Livox Mid 360 LiDAR unit to enable simultaneous localization and mapping (SLAM) using Kiss ICP and Open 3D (or PyQT) for 3D interactive visualization. A new PyQT-based GUI offers smoother keyboard control with press-and-release functionality. A critical hanger boot sequence fix prevents the robot from falling during startup. Challenges persist with wired Ethernet and Wi-Fi connectivity due to the G1's three onboard computers (Jetson, LiDAR, control unit), particularly for the RGB camera. Initial attempts at occupancy grid generation and path planning show promise but require further refinement, with future work focusing on robust navigation, arm and Dex/Inspire hand control, and visual language model integration for advanced object identification and task execution.

Key takeaway

For robotics engineers developing navigation and manipulation for humanoid robots like the Unitree G1, prioritize robust SLAM integration and boot sequence logic. You should account for multi-computer network complexities and LiDAR mounting orientations early in your design. Consider lightweight SLAM solutions like Kiss ICP for Python-based implementations. Future efforts should focus on refining occupancy grids and path planning, alongside integrating visual language models for advanced object identification and arm/hand control to enable complex task execution.

Key insights

Integrating LiDAR and SLAM on a humanoid robot requires careful software stack management and addresses core navigation challenges.

Principles

• Robot head camera angles often default to downward tasks.
• LiDAR unit placement is critical for accurate localization.
• Complex robot systems demand robust boot sequences.

Method

Integrate Livox Mid 360 LiDAR using its SDK. Implement SLAM with Kiss ICP for odometry and Open 3D/PyQT for visualization. Develop a PyQT GUI for keyboard control and robot command issuance.

In practice

• Use Kiss ICP for lightweight Python-based SLAM.
• Implement a pre-startup balance check for robot safety.
• Consider LLMs for rapid code generation in robotics.

Topics

• Unitree G1
• Humanoid Robotics
• LiDAR SLAM
• Robot Navigation
• Kiss ICP
• Visual Language Models

Best for: Robotics Engineer, AI Engineer, Software Engineer

Related on AIssential

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• HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers — HANDOFF uses distilled multi-teacher control for robust, intuitive humanoid whole-body command, enabling diverse manipulation and locomotion.
• Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking — The framework enables terrain-agnostic 3D humanoid foothold tracking by dynamically sampling goals and using a robust target representation for real-world transfer.
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Editorial summary, takeaway, and curation by AIssential. Original article published by sentdex.