> For the complete documentation index, see [llms.txt](https://docs.openmind.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.openmind.com/release-notes/om1-ros2-sdk/v1.0.x.md). # v1.0.x Production ready release for the Docker image openmindagi/om1\_ros2\_sdk, which provides the full ROS2 system for running the Unitree SDK. Covers all v1.0 patch releases including initial launch and bug fixes. ### **v1.0.1** - latest * Added support for both Isaac Sim and Gazebo for Unitree Go2 and Unitree G1 * Added support for the LimX Tron robot * Improved OM Path and reduced computational resource usage ### **v1.0.0** #### Go2 LiDAR Localization Introduced a custom LiDAR localization node into the navigation launch pipeline, replacing the previous AMCL-based localization. Package dependencies and entry points have been updated to support the new implementation. #### Localization via Zenoh Enhanced the localization service and configuration to support additional topics and service endpoints. Updated service paths and expanded the Zenoh bridge configuration to improve localization data handling. #### NumPy Compatibility Update Downgraded NumPy to maintain compatibility with tf\_transformations. #### Risk-Aware Area Avoidance Added a risk-aware local map and path selection mechanism to help the robot avoid unsafe areas such as holes, drops, steep terrain, and unknown regions. This improves navigation safety, simplifies tuning, and enables easier debugging through RViz. #### Enhanced Localization Parameters * Reduced global localization particles from 10,000 to 5,000 * Added parameters for minimum prediction confidence, maximum consecutive failures, and failure quality thresholds * Implemented consecutive failure tracking with automatic re-localization * Updated logging to expose confidence and failure metrics * Reduced Localization CPU Usage * Optimized obstacle expansion logic in go2\_lidar\_localization.py by replacing a custom gradient mask loop with scipy.ndimage.maximum\_filter, improving performance, maintainability, and code clarity. #### LiDAR Rotation Fix Fixed the issue affecting LiDAR rotation handling to improve localization accuracy. #### Adjusted Movement Policy Updated movement policy logic to improve navigation behavior and stability. #### Launch & System Architecture * Odom Relay Node Refactor * Moved the odometry relay node from the navigation and SLAM launch files into the sensor launch file. This centralizes relay functionality and improves reliability by enabling automatic restarts on failure. #### Unitree G1 Support Added Added full support for Unitree G1, including: * Navigation and robot behavior configuration * SLAM configuration * Robot state publishing and visualization #### Gazebo Integration Fully integrated Gazebo simulation with OM1. Added joystick control, SLAM simulation support, and a simulated RealSense D435 camera for the Go2 robot, along with corresponding Gazebo sensors and ROS bridge topics. #### Frontier-Based Exploration Added a new ROS 2 package, frontier\_explorer, implementing a frontier-based exploration algorithm. This includes core exploration logic, costmap management, configuration files, launch files, and ROS 2 package setup. The package is now integrated with OM1, with updated simulation and configuration parameters and removal of an obsolete submodule reference. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.openmind.com/release-notes/om1-ros2-sdk/v1.0.x.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.